, i% z5 d3 w; R! Q/ R ├─001_machine_learning_by_andrew_ng_coursera+ r/ B! ~4 c* ?+ R
│ └─001_machine_learning_by_andrew_ng_coursera
- U$ |. x; ^8 T, H1 s/ m │ ├─ml-003! e7 ^9 ^$ J$ a5 Y5 M7 r
│ │ ├─01_I._Introduction
! N. r0 w) X6 s9 s* N, `3 I │ │ │ 01_Welcome.mp4
5 M3 Q: g$ t: m4 I& H2 S' Q: p& P4 i0 ~ │ │ │ 01_Welcome.pdf. O" ~: z, r3 C4 L ]3 Z
│ │ │ 01_Welcome.pptx
. e2 i! P+ y! } │ │ │ 01_Welcome.srt
5 s1 e# h+ t7 Z0 B- h* Q$ R │ │ │ 01_Welcome.txt, k2 v9 M8 R$ F6 h. P' Z1 f# J
│ │ │ 02_What_is_Machine_Learning.mp4
% H& _% \& @* R4 D │ │ │ 02_What_is_Machine_Learning.srt7 `2 n {- U. Y: v! D; c
│ │ │ 02_What_is_Machine_Learning.txt3 D% [3 J ~: g; C
│ │ │ 03_Supervised_Learning.mp4
) y& j6 L, }! e: Z │ │ │ 03_Supervised_Learning.srt' f) w: C9 Y2 ]1 X# v5 g f' o
│ │ │ 03_Supervised_Learning.txt4 g; D8 R2 `* Z8 [0 V- n$ I1 w. J
│ │ │ 04_Unsupervised_Learning.mp4+ g7 c) d" x5 S! D7 G, Z
│ │ │ 04_Unsupervised_Learning.srt
! H- K, ^' O% S. R/ }; J) q% Q │ │ │ 04_Unsupervised_Learning.txt
+ |) v$ u0 s, j; J) B1 k │ │ │
8 \! ~/ J% E! d) M │ │ ├─02_II._Linear_Regression_with_One_Variable" p6 K) o8 G, a, s
│ │ │ 02_Cost_Function.mp4/ R9 p1 F# L U) Q; }/ |# w
│ │ │ 02_Cost_Function.srt
& D9 ]2 |5 e$ L2 r- w4 h$ p │ │ │ 02_Cost_Function.txt2 T& ^ D0 w2 @ V& D( C4 z
│ │ │ 05_Gradient_Descent.mp4
% ]( g) X& d4 ?2 y6 \, K# d, D( I │ │ │ 05_Gradient_Descent.srt: T" ~5 H' U+ w. A7 N1 l
│ │ │ 05_Gradient_Descent.txt
, }( I+ i% n8 n7 J: E │ │ │ 08_Whats_Next.mp4
9 A) m6 n6 s+ D$ _! `0 q │ │ │ 08_Whats_Next.srt/ Z' _8 ]# o4 c* F% {
│ │ │ 08_Whats_Next.txt( [2 ]+ v4 d! ^2 J9 \: @
│ │ │ & K. u" |' v/ v6 Z, z ^
│ │ ├─03_III._Linear_Algebra_Review
" I$ a2 r# D/ _1 u4 V │ │ │ 01_Matrices_and_Vectors.mp4
5 X3 y5 Y" @, z5 G. p │ │ │ 01_Matrices_and_Vectors.pdf0 R: E: g# m2 t
│ │ │ 01_Matrices_and_Vectors.pptx" ^: b$ v# W9 J$ @, C
│ │ │ 01_Matrices_and_Vectors.srt; ` v" G W( h) [/ F4 _
│ │ │ 01_Matrices_and_Vectors.txt3 |. X+ t; b$ _, u! ]
│ │ │ 03_Matrix_Vector_Multiplication.mp4; N# q% J8 R* ^/ b: J
│ │ │ 03_Matrix_Vector_Multiplication.srt7 T5 i; l- b' H Y+ |! z
│ │ │ 03_Matrix_Vector_Multiplication.txt: t: F1 I- s; s) J4 ?+ @. h5 f
│ │ │ 04_Matrix_Matrix_Multiplication.mp4% A H( \: h% b6 Z/ E! Y& M: N
│ │ │ 04_Matrix_Matrix_Multiplication.srt3 h. r" W; [- Y/ z; Z( [% s
│ │ │ 04_Matrix_Matrix_Multiplication.txt
# H2 h# m( L8 @& v │ │ │ 05_Matrix_Multiplication_Properties.mp4
: g+ |/ @! f* F8 j# r │ │ │ 05_Matrix_Multiplication_Properties.srt& V$ }3 H/ O; f
│ │ │ 05_Matrix_Multiplication_Properties.txt
( N. I3 M# I' a0 j3 U! K │ │ │ 06_Inverse_and_Transpose.mp48 q/ ?0 U. E h) O1 X
│ │ │ 06_Inverse_and_Transpose.srt& D! j) U6 p L) A$ W) t
│ │ │ 06_Inverse_and_Transpose.txt) ~, u S; v. I1 T2 [3 B# v" ]7 \
│ │ │ & f" H& r5 k4 v* S& D) ~7 P: w7 y
│ │ ├─04_IV._Linear_Regression_with_Multiple_Variables
m4 V* b* R L% q: x │ │ ├─05_V._Octave_Tutorial
3 N' B4 K9 |- P3 B: }) l │ │ │ 01_Basic_Operations.mp4 _+ Q/ K4 |9 f3 p7 u! t6 q2 q
│ │ │ 01_Basic_Operations.pdf
0 S# C1 P' P% ? │ │ │ 01_Basic_Operations.pptx
1 _* k1 r3 A0 d; N+ E k9 I, J │ │ │ 01_Basic_Operations.srt7 X! Z8 R3 Y2 B2 b: j
│ │ │ 01_Basic_Operations.txt2 @; \+ [* X9 G, G
│ │ │ 02_Moving_Data_Around.mp47 ~4 ^0 }' R% S. G: X
│ │ │ 02_Moving_Data_Around.srt
- ]) v; s& q% a' a$ U │ │ │ 02_Moving_Data_Around.txt- a- l3 [2 d9 V: O6 [7 o% m3 r
│ │ │ 03_Computing_on_Data.mp4! d$ R# p* o# h) s2 D
│ │ │ 03_Computing_on_Data.srt" \& {' ~/ i2 A$ Y4 w
│ │ │ 03_Computing_on_Data.txt
( `1 H u7 T* X u- t2 Q& H" {( t3 B │ │ │ 04_Plotting_Data.mp4- Q9 L( ~9 p4 a6 m3 d! H
│ │ │ 04_Plotting_Data.srt
7 p. R, K4 o2 s( M& w) r6 t$ O │ │ │ 04_Plotting_Data.txt3 B" P' U* e+ _: a" z" K, ^
│ │ │ 06_Vectorization.mp4
6 ~8 h8 B* T8 n, E │ │ │ 06_Vectorization.srt3 V) L' j# N, E( l. R* o" k
│ │ │ 06_Vectorization.txt5 R+ S5 b5 A2 y; r& q- j2 k8 w
│ │ │
; w2 ~7 }7 z9 j; G! @* S │ │ ├─06_VI._Logistic_Regression: h1 k- O5 v! l, L; Q
│ │ │ 01_Classification.mp4. _# L- D F5 S) U% B/ O1 {; B
│ │ │ 01_Classification.pdf9 `5 e- ~8 {- h
│ │ │ 01_Classification.pptx
5 U$ n# P0 L4 M) Z │ │ │ 01_Classification.srt
& B4 q' s) I- G! `' v, A │ │ │ 01_Classification.txt" K7 i( C! y2 N& v R$ r: V. B, I# z
│ │ │ 02_Hypothesis_Representation.mp47 D# P* b" {7 m# F" H
│ │ │ 02_Hypothesis_Representation.srt
. e+ y) `8 F* _, ~1 c$ | │ │ │ 02_Hypothesis_Representation.txt
) ^ R: [! x& b3 w& b │ │ │ 03_Decision_Boundary.mp41 v$ O8 N) [* B' c' [
│ │ │ 03_Decision_Boundary.srt# M$ p Y. x; p) ~8 A2 U" T4 P
│ │ │ 03_Decision_Boundary.txt8 F+ |& a p) B+ C- W- M
│ │ │ 04_Cost_Function.mp4- K4 ^( R$ q8 f0 K J
│ │ │ 04_Cost_Function.srt7 X' k/ V- l0 R
│ │ │ 04_Cost_Function.txt
. Y* q4 @/ o' ~1 W │ │ │ 06_Advanced_Optimization.mp4
; U7 g" [' N( B/ l) R │ │ │ 06_Advanced_Optimization.srt' o( q9 ?0 @+ [
│ │ │ 06_Advanced_Optimization.txt! b* c) H- ?* l4 E+ d
│ │ │
7 Z2 N+ v/ t5 h2 W5 a │ │ ├─07_VII._Regularization
6 P9 q% v* l2 G9 h6 \8 k; @ │ │ │ 01_The_Problem_of_Overfitting.mp4
0 p# a9 ~5 ~- @ │ │ │ 01_The_Problem_of_Overfitting.pdf; W0 O% ?% J w. ]3 l
│ │ │ 01_The_Problem_of_Overfitting.pptx# h9 g, E: g& Q8 a; V9 ?: |
│ │ │ 01_The_Problem_of_Overfitting.srt
+ C4 Y) V8 u! A+ z │ │ │ 01_The_Problem_of_Overfitting.txt/ ^1 M9 l* r4 w7 v
│ │ │ 02_Cost_Function.mp44 R, A0 q" a. r$ W
│ │ │ 02_Cost_Function.srt8 \; n" l+ G1 s, v6 d3 Y
│ │ │ 02_Cost_Function.txt
9 F4 T/ I1 o4 J. ~: x; e5 N" T │ │ │ 03_Regularized_Linear_Regression.mp4: m# j4 _" y e! s
│ │ │ 03_Regularized_Linear_Regression.srt9 i8 Z& f. U1 y4 S7 Y
│ │ │ 03_Regularized_Linear_Regression.txt
7 v; v* }/ K; P' g @) F │ │ │ 04_Regularized_Logistic_Regression.mp4
6 k3 n. l# |8 a3 W& O$ B │ │ │ 04_Regularized_Logistic_Regression.srt
$ u! l, F- Y8 I+ w1 @" T3 b$ q │ │ │ 04_Regularized_Logistic_Regression.txt( m$ C. ~' G6 o' k8 k
│ │ │
, U+ g+ w6 X) I, E │ │ ├─08_VIII._Neural_Networks-_Representation
. q$ z5 J& t1 w! ?5 I3 o │ │ │ 01_Non-linear_Hypotheses.mp4( ]" e5 o7 M' _. _
│ │ │ 01_Non-linear_Hypotheses.pdf3 a. H' T9 j, t
│ │ │ 01_Non-linear_Hypotheses.srt
$ ^5 O8 Q( d1 r+ _8 I8 q │ │ │ 01_Non-linear_Hypotheses.txt
# A7 E, T% X, R │ │ │ 02_Neurons_and_the_Brain.mp4
5 }/ f2 P5 G, Y% H │ │ │ 02_Neurons_and_the_Brain.srt) p; l6 ]) F d7 ?/ b3 S" j# U# h
│ │ │ 02_Neurons_and_the_Brain.txt9 J- p8 K# u4 Z2 K
│ │ │
+ w6 D/ l" P- K2 R │ │ ├─09_IX._Neural_Networks-_Learning- D7 z3 R/ h- `
│ │ │ 01_Cost_Function.mp46 O, A8 @0 e H9 |" R' Z1 P
│ │ │ 01_Cost_Function.pdf1 x! V/ Q' I, B* n0 c, ~
│ │ │ 01_Cost_Function.pptx
: m: R& i8 t, H ]9 u8 W │ │ │ 01_Cost_Function.srt
) D; O: w, `& X6 [/ { │ │ │ 01_Cost_Function.txt; G! N, s8 P/ ^4 X5 Y6 V( M' B
│ │ │ 02_Backpropagation_Algorithm.mp4
4 q s$ k! Z3 G4 P1 Y │ │ │ 02_Backpropagation_Algorithm.srt- I/ `9 ?! p( U0 q) _3 Y6 N
│ │ │ 02_Backpropagation_Algorithm.txt! ~ @: X7 W8 X) |2 o. v; e2 [
│ │ │ 03_Backpropagation_Intuition.mp4
$ \; X1 d; ]2 h6 u C* k │ │ │ 03_Backpropagation_Intuition.srt
* y+ b# b( n3 H: K- H" w0 Q │ │ │ 03_Backpropagation_Intuition.txt
$ |+ p6 t6 Z0 `: @& T; |! n │ │ │ 05_Gradient_Checking.mp4, y9 L7 e' H/ v9 }, E
│ │ │ 05_Gradient_Checking.srt
0 U( D8 n9 x+ d6 v; R │ │ │ 05_Gradient_Checking.txt
0 G: C( i& I" C( ]2 H │ │ │ 06_Random_Initialization.mp46 Y4 u% E. l" e- c" z9 }
│ │ │ 06_Random_Initialization.srt
( V5 I$ e% w: X% ~* n) p4 A9 L7 I' c/ g* i │ │ │ 06_Random_Initialization.txt% r& x! U1 E# H4 p4 }5 R
│ │ │ 07_Putting_It_Together.mp47 ~; I* q: V) Z$ ~' i! w2 @' P' @
│ │ │ 07_Putting_It_Together.srt
/ p% @9 ~! n+ {; M │ │ │ 07_Putting_It_Together.txt
& `- I0 l: [# E- J │ │ │ 08_Autonomous_Driving.mp43 U; ~; z, C' z9 z
│ │ │ 08_Autonomous_Driving.srt
9 H" |6 w8 a: V │ │ │ 08_Autonomous_Driving.txt. A' i; o* H9 a7 Q6 M4 L& Z% ^- @
│ │ │ 4 r2 C2 V# T7 B3 J+ Q4 |
│ │ ├─10_X._Advice_for_Applying_Machine_Learning
& Z3 }, `# O/ x9 ?: n │ │ │ 06_Learning_Curves.mp4
: U! @* y) b5 d. ?3 v5 I! b) s/ p$ B │ │ │ 06_Learning_Curves.srt3 l% ^: r% G; s* C. z& }2 U; `
│ │ │ 06_Learning_Curves.txt
. C }. U& Q# B+ d+ x5 z │ │ │
6 | O2 @, N- }8 Y9 u" B4 Y │ │ ├─11_XI._Machine_Learning_System_Design3 h! u. }/ X1 s1 E# @# G) P
│ │ │ 02_Error_Analysis.mp4
$ L: ~, c) C( R% E │ │ │ 02_Error_Analysis.srt
e3 K5 J* T' @5 f) q │ │ │ 02_Error_Analysis.txt/ s7 v k; s) N! Z7 r, n
│ │ │
! @% d! y1 t+ I │ │ ├─12_XII._Support_Vector_Machines: ?: j0 t; \8 o* D# {
│ │ │ 01_Optimization_Objective.mp4% D$ G6 ^* @3 H' Q/ G
│ │ │ 01_Optimization_Objective.pdf, _' n, t6 f) L+ o- U
│ │ │ 01_Optimization_Objective.pptx# Q% o: e" N) ]/ J$ e/ i& |( l
│ │ │ 01_Optimization_Objective.srt
9 ]& v, W6 B; n8 j0 P │ │ │ 01_Optimization_Objective.txt9 P5 z: F6 D3 }( j
│ │ │ 02_Large_Margin_Intuition.mp48 @3 d+ T4 A7 T$ H2 |. G! m0 W$ P2 O
│ │ │ 02_Large_Margin_Intuition.srt5 v* D# n( W* l5 b+ ?$ P
│ │ │ 02_Large_Margin_Intuition.txt) Q9 C$ M7 K, E; X
│ │ │ 04_Kernels_I.mp4
" t% `' Z4 [$ E: w+ K │ │ │ 04_Kernels_I.srt- Y- L& Y6 W! C7 o" F
│ │ │ 04_Kernels_I.txt
/ j5 y) A% `6 E1 j u │ │ │ 05_Kernels_II.mp4
7 B2 ~; }6 a3 ] │ │ │ 05_Kernels_II.srt
; @+ ?0 {1 j4 o5 G │ │ │ 05_Kernels_II.txt3 q1 [4 T* b7 `! S1 \( r+ e* J
│ │ │ 06_Using_An_SVM.mp4" V5 `. j0 w! h. f( @% e
│ │ │ 06_Using_An_SVM.srt
5 n' U2 `' H% r+ c+ U% D# ^) Y │ │ │ 06_Using_An_SVM.txt
# K3 B& g% F6 L& P# m+ O │ │ │ 4 |9 Q0 ]5 U0 R9 p% ?
│ │ ├─13_XIII._Clustering
$ C7 ^: {$ X/ S │ │ │ 01_Unsupervised_Learning-_Introduction.mp45 n. w' w) z- G" \* Y
│ │ │ 01_Unsupervised_Learning-_Introduction.pdf
1 c& E' W6 c( x/ {1 B │ │ │ 01_Unsupervised_Learning-_Introduction.pptx n& H5 |2 ~: z9 q5 Q
│ │ │ 01_Unsupervised_Learning-_Introduction.srt2 E2 s* c! [" m
│ │ │ 01_Unsupervised_Learning-_Introduction.txt
0 Z; F; z( t! j' m$ j+ i, U+ v │ │ │ 02_K-Means_Algorithm.mp4, V8 h2 d7 T, F1 l4 b1 K, q
│ │ │ 02_K-Means_Algorithm.srt1 J3 y) S$ {1 S. @& y
│ │ │ 02_K-Means_Algorithm.txt: b3 A f2 e5 O3 e4 ~+ n% J
│ │ │ 03_Optimization_Objective.mp47 g, {& S" D% n2 C4 E. O& K$ f4 b4 b
│ │ │ 03_Optimization_Objective.srt! r1 u1 z8 Y" f9 K
│ │ │ 03_Optimization_Objective.txt2 v9 g" r2 d$ d- j2 D1 M
│ │ │ 04_Random_Initialization.mp49 }' z, j3 ]7 Z/ W; t( u1 i
│ │ │ 04_Random_Initialization.srt
0 |# ^) v/ T: W! k9 ` │ │ │ 04_Random_Initialization.txt
* r! B/ W" _: G8 ~ │ │ │ 05_Choosing_the_Number_of_Clusters.mp4* g+ J+ K2 m/ l" c. U
│ │ │ 05_Choosing_the_Number_of_Clusters.srt' g+ P5 |6 \3 {5 }, w
│ │ │ 05_Choosing_the_Number_of_Clusters.txt; R: f) C4 x* J' o( Y
│ │ │
4 v( }/ H' n, q. v │ │ ├─14_XIV._Dimensionality_Reduction
8 c1 `) }0 S/ m' ]( ?2 n │ │ │ 02_Motivation_II-_Visualization.mp4
8 a. ]5 f$ Q p8 v& h. ~7 E/ k │ │ │ 02_Motivation_II-_Visualization.srt
2 X% c9 \9 |: u9 t/ x0 q │ │ │ 02_Motivation_II-_Visualization.txt
9 q% T. F1 }) m: U │ │ │ 07_Advice_for_Applying_PCA.mp4" J& \2 a, ?) M+ |# `* n* S9 X
│ │ │ 07_Advice_for_Applying_PCA.srt2 q# o* [+ U7 M; p1 t
│ │ │ 07_Advice_for_Applying_PCA.txt
2 p6 V) G( X2 n6 ^6 y# I% _" a( M │ │ │
s, l: ]# h- F( t) @ \" {7 A( W │ │ ├─15_XV._Anomaly_Detection
: W, q6 g7 Y! ]# I7 u: i' Z2 j2 q3 ^ │ │ │ 01_Problem_Motivation.mp4; Q- t/ j1 `/ M; b' P! n0 e9 q% p
│ │ │ 01_Problem_Motivation.pdf
2 M4 r# L4 s3 a0 t6 e( l │ │ │ 01_Problem_Motivation.pptx/ b3 B3 h6 W9 ~% x/ [; i' Y6 D# H1 f
│ │ │ 01_Problem_Motivation.srt9 a0 [5 m! C |- ]9 M$ W3 f o
│ │ │ 01_Problem_Motivation.txt0 q: Y9 N) V5 `7 X+ _. E! p
│ │ │ 02_Gaussian_Distribution.mp4
. }. Q# y1 |. y# [! e$ j' M │ │ │ 02_Gaussian_Distribution.srt
b% w* f2 \3 J/ ]( H │ │ │ 02_Gaussian_Distribution.txt9 J" z% M/ f6 R; H W
│ │ │ 03_Algorithm.mp4
. M6 K4 R4 C) J │ │ │ 03_Algorithm.srt
6 x: J* [( y6 ^& b1 C4 | │ │ │ 03_Algorithm.txt5 b1 N+ [$ p. p5 S+ N7 G
│ │ │ 06_Choosing_What_Features_to_Use.mp4
, o) H9 E9 A* ~" f │ │ │ 06_Choosing_What_Features_to_Use.srt, _' W- l; o5 l1 w+ z. u
│ │ │ 06_Choosing_What_Features_to_Use.txt
; H `( g6 P9 P/ U) o │ │ │
7 W1 k- l, ]" u) @- P │ │ ├─16_XVI._Recommender_Systems7 z8 s) Q" i7 j& q
│ │ │ 01_Problem_Formulation.mp4
9 h( k( R' N! I, L+ A │ │ │ 01_Problem_Formulation.pdf3 t" M% p1 z2 ~
│ │ │ 01_Problem_Formulation.pptx9 }6 U m, T5 Y2 V o* K
│ │ │ 01_Problem_Formulation.srt O$ A. M/ @/ y+ o/ k9 ?
│ │ │ 01_Problem_Formulation.txt
% |' ^; E( @( M& w │ │ │ 02_Content_Based_Recommendations.mp4
& U+ e/ O2 P# E. e+ x1 z │ │ │ 02_Content_Based_Recommendations.srt
6 h! B0 C4 @: n( U1 E; ?0 O" i% s │ │ │ 02_Content_Based_Recommendations.txt
+ U9 F. i5 W: K, u+ s3 f' y │ │ │ 03_Collaborative_Filtering.mp4
9 ?- U: U6 j0 d! b" r; m- o │ │ │ 03_Collaborative_Filtering.srt
/ u" w, G5 Y$ j( w │ │ │ 03_Collaborative_Filtering.txt
! l8 @! O/ d* ` W! o; }' s9 z' b% e │ │ │ 04_Collaborative_Filtering_Algorithm.mp4# W1 b: N5 `: z: L
│ │ │ 04_Collaborative_Filtering_Algorithm.srt
" @, T5 P) q7 f% |1 P │ │ │ 04_Collaborative_Filtering_Algorithm.txt ?" x0 ^1 O( @: }. X$ Q
│ │ │
+ A1 d" O/ g& B6 n. Q: U │ │ ├─17_XVII._Large_Scale_Machine_Learning# c$ u) F; {# P' b7 X7 t
│ │ │ 05_Online_Learning.mp4
: I/ ~( o, c f7 E* s │ │ │ 05_Online_Learning.srt% r, h" ^; O% `3 Y
│ │ │ 05_Online_Learning.txt
4 c" w6 x0 M: h% u │ │ │ " ~2 g+ |, C0 o7 ]; Y+ J
│ │ ├─18_XVIII._Application_Example-_Photo_OCR9 E5 C" ~6 `% w- M4 J9 x
│ │ │ 02_Sliding_Windows.mp40 _. T' F; [, z/ S* X* P2 g8 b
│ │ │ 02_Sliding_Windows.srt
- Y# e/ Q" u! F$ z5 q" g8 G │ │ │ 02_Sliding_Windows.txt0 ?; [# N. s7 ~6 l
│ │ │ ' q) Z; { T/ s4 b$ [" s' x# H
│ │ └─19_XIX._Conclusion
: I# x. i' o( J- i: `1 x& O │ │ 01_Summary_and_Thank_You.mp4% h [9 J: M5 ~; a* {/ P9 T6 P2 p
│ │ 01_Summary_and_Thank_You.srt/ z7 a; A5 P8 L h: e: c
│ │ 01_Summary_and_Thank_You.txt
! N: k& _. p! [# d' j) v( A │ │
3 f/ d! m* a; A0 X% ^ │ └─机器学习-斯坦福-Andrew NG-20122 P8 ?6 t) y# A) o
│ │ 下载说明.txt% G2 X1 p# a5 J. R9 e8 S q2 o
│ │ 关注我们.png! M6 {$ w* }7 C0 f! ?: n% i' C" n: a
│ │ 攻城狮论坛=网络技术+编程视频.url) `4 ?3 P5 q; ?+ v) p
│ │ 机器学习笔记2012_v0.1.pdf
2 K" b$ E, S$ W │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt6 g0 Z2 D' z6 b, e* \% ~
│ │
5 l. c! {" a) _, u9 q( f4 a. ] │ ├─week 14 ?9 t4 l/ w" G7 N0 w6 r) P
│ │ 1 - 1 - Welcome (7 min).mp4, G/ t6 O0 G( @6 }! d9 q
│ │ 1 - 1 - Welcome (7 min).srt2 i9 V# `0 A1 t ]6 r4 E7 E
│ │ 1 - 2 - What is Machine Learning (7 min).mp4
! O; d6 W1 L! f │ │ 1 - 2 - What is Machine Learning- (7 min).srt( J% E* {: H5 A) }) e# y
│ │ 1 - 3 - Supervised Learning (12 min).mp4* s# s( {6 U: V- g* x" F/ Q
│ │ 1 - 3 - Supervised Learning (12 min).srt
% I- n3 U& ]1 Y& ~' ?, W │ │ 1 - 4 - Unsupervised Learning (14 min).mp4; M2 b, `$ A, z4 v
│ │ 1 - 4 - Unsupervised Learning (14 min).srt" O; G& Z5 B8 J
│ │ 2 - 1 - Model Representation (8 min).mp4
. d& c$ y! V2 d) B! b │ │ 2 - 1 - Model Representation (8 min).srt ]5 J+ q+ d- u$ M) v( R0 J, V
│ │ 2 - 2 - Cost Function (8 min).mp4
) a% \# M& t0 E$ v# t4 n, b │ │ 2 - 2 - Cost Function (8 min).srt
- T, M0 E( b+ X/ n9 L2 p8 c' f │ │ 2 - 5 - Gradient Descent (11 min).mp46 B1 V, z6 q4 J
│ │ 2 - 5 - Gradient Descent (11 min).srt6 n" i6 p# u d
│ │ 2 - 8 - What's Next (6 min).srt0 ~9 ]( T. ?' h8 r
│ │ 2 - 8 - Whats Next (6 min).mp4
4 y# r. L( t: g' _/ q& u' b* D │ │ 3 - 1 - Matrices and Vectors (9 min).mp4; o- N) v. q s7 Z
│ │ 3 - 1 - Matrices and Vectors (9 min).srt
\' F# S/ [1 E( C: r- y │ │
6 G9 s0 a, y- R- I1 d5 a │ ├─week 102 e0 m! q8 [/ D3 \
│ │ 17 - 5 - Online Learning (13 min).mp4
" [$ e$ b* c7 d' g! J │ │ 17 - 5 - Online Learning (13 min).srt; o [7 ]5 T- a% L) r- l1 Z. Z. x
│ │ Lecture17.pdf
$ v8 z( R% S/ C: O │ │ ) @% Z; {( C9 _0 s W2 D
│ ├─week 2
, O0 F% u, c' e: Q- b │ │ 4 - 1 - Multiple Features (8 min).mp4" _, E {+ q& X, j. h8 B
│ │ 4 - 1 - Multiple Features (8 min).srt; b% w& m4 S% Q; n# g# k0 ]
│ │ 4 - 6 - Normal Equation (16 min).mp4& F$ J# y, R1 {/ l' T
│ │ 4 - 6 - Normal Equation (16 min).srt
* l/ j' m! @9 W │ │ 5 - 1 - Basic Operations (14 min).mp4
1 K( G' I) k2 i) L- N: f, { │ │ 5 - 1 - Basic Operations (14 min).srt! t; h9 g4 c( f* W0 `
│ │ 5 - 2 - Moving Data Around (16 min).mp4; D B G' o8 J+ R! B6 U5 X8 D0 g
│ │ 5 - 2 - Moving Data Around (16 min).srt
O5 A# K8 B, o) b │ │ 5 - 3 - Computing on Data (13 min).mp4
# @6 {# b. k& |: s) N% c& H │ │ 5 - 3 - Computing on Data (13 min).srt& R4 N$ P) n, A3 \
│ │ 5 - 4 - Plotting Data (10 min).mp4
6 G4 h% A. N7 T$ b9 I │ │ 5 - 4 - Plotting Data (10 min).srt
?. C9 a1 _0 U+ j. [) T │ │ 5 - 6 - Vectorization (14 min).mp4
* W% r( \* n9 H& e- ?3 ` │ │ 5 - 6 - Vectorization (14 min).srt
2 Q( Z! Y/ m6 }" ]+ H+ I$ ` │ │ Lecture4.pdf
" @# a; M/ u2 `- P' R │ │ Lecture5_octave tutorial.pdf( V/ j; m D% I3 } ~3 p9 {% H
│ │
( H$ M0 b1 z- D* m │ ├─week 3" D, n3 J! ?: E4 K, c
│ │ 6 - 1 - Classification (8 min).mp4
2 j6 F7 J. S4 i4 d. @+ A+ b │ │ 6 - 1 - Classification (8 min).srt: O2 o4 D1 f0 D# n! i G4 U$ e# N3 y
│ │ 6 - 2 - Hypothesis Representation (7 min).mp4 I0 i1 a' Y# `5 V% C1 O4 W
│ │ 6 - 2 - Hypothesis Representation (7 min).srt
2 X' _8 L( D- Y. O │ │ 6 - 3 - Decision Boundary (15 min).mp48 `+ i2 x8 [% w/ u u7 i% l
│ │ 6 - 3 - Decision Boundary (15 min).srt
/ X' b0 l( f" e7 o$ r$ | │ │ 6 - 4 - Cost Function (11 min).mp4- M8 K+ _+ Z+ M6 E( F
│ │ 6 - 4 - Cost Function (11 min).srt
0 Y# i) I* e$ L, r/ K │ │ 6 - 6 - Advanced Optimization (14 min).mp42 Z; O, g9 }9 J& }8 N5 K; `
│ │ 6 - 6 - Advanced Optimization (14 min).srt
! B; o4 Z; w$ S; {* H* e │ │ 7 - 2 - Cost Function (10 min).mp46 K Q4 @" j+ p# Z9 ~
│ │ 7 - 2 - Cost Function (10 min).srt3 G4 K( k1 o2 }- R7 D. m# p. e
│ │ docs_slides_Lecture6.pdf
$ [0 C, @8 ^" k6 Z8 V0 b5 D! O3 b0 \ │ │ docs_slides_Lecture7.pdf
2 F! k# M' z$ J7 ]; u" p │ │
8 U. C6 n& F1 [) X/ Q │ ├─week 4; q3 k5 C6 d- |* [+ n; \6 M
│ │ 8 - 1 - Non-linear Hypotheses (10 min).mp4
" }* _' Y( K# A- D! z' U" h; I │ │ 8 - 1 - Non-linear Hypotheses (10 min).srt/ @8 ]4 C9 w, d8 t# J
│ │ 8 - 2 - Neurons and the Brain (8 min).mp4. |% K& r( p* Z' H( P
│ │ 8 - 2 - Neurons and the Brain (8 min).srt: p1 z- R" ~( N5 H( u- H( Z8 s$ q
│ │ 8 - 3 - Model Representation I (12 min).mp4% a# F3 {* i0 i4 j0 x7 U) ?
│ │ 8 - 3 - Model Representation I (12 min).srt7 ]+ C9 z9 Y& c
│ │ 8 - 4 - Model Representation II (12 min).mp4) d! B( p/ u! b; w* |
│ │ 8 - 4 - Model Representation II (12 min).srt
, B4 j9 h+ H+ a8 Y7 m% H9 x9 w, | │ │ 8 - 5 - Examples and Intuitions I (7 min).mp4! B, V l& a* q. O0 e# S& L
│ │ 8 - 5 - Examples and Intuitions I (7 min).srt
# m# H2 r Y J │ │ 8 - 7 - Multiclass Classification (4 min).mp4& M6 |/ M' F: Q0 Z' W- _
│ │ 8 - 7 - Multiclass Classification (4 min).srt3 w; B$ X2 _6 Y l: W1 m' E
│ │ docs_slides_Lecture8.pdf
/ K2 H( f% @0 ~# _ │ │ / { [# K( U0 e, U5 B+ l
│ ├─week 5
; \4 U7 y) r2 |/ S0 b! {9 {" C │ │ 9 - 1 - Cost Function (7 min).mp41 \ s% _4 j! O9 G
│ │ 9 - 1 - Cost Function (7 min).srt4 F R3 |: N5 g4 C0 s& `1 a
│ │ 9 - 5 - Gradient Checking (12 min).mp45 c/ L" s6 t! p' c4 V1 ?
│ │ 9 - 5 - Gradient Checking (12 min).srt2 v' @5 @- b$ a; n9 ^ c' F# y8 c' N
│ │ 9 - 6 - Random Initialization (7 min).mp4# G9 g# H* m9 b2 d2 K6 I0 ~' u
│ │ 9 - 6 - Random Initialization (7 min).srt! i6 F6 g! }& a9 f8 |! A; l
│ │ 9 - 7 - Putting It Together (14 min).mp4
- S( T1 o) }- p! m0 A& F │ │ 9 - 7 - Putting It Together (14 min).srt
* W! U! t) u& w' G │ │ 9 - 8 - Autonomous Driving (7 min).mp4
) Y( b' c. O7 _$ ^ │ │ 9 - 8 - Autonomous Driving (7 min).srt( _7 ~2 D k5 t8 L6 B% s0 \+ Q0 s( X
│ │ docs_slides_Lecture9.pdf
* h8 b& b9 }: v) t" a1 f │ │
# c( J* w- p- u- _2 J │ ├─week 6
. G$ S+ J, X3 K4 z │ │ 10 - 2 - Evaluating a Hypothesis (8 min).mp4
) S6 q S* d' m! `# {( K1 _ │ │ 10 - 2 - Evaluating a Hypothesis (8 min).srt" y$ a8 N( L; V/ R1 z
│ │ 10 - 6 - Learning Curves (12 min).mp4
( S6 j5 @& P/ }: z! e9 q H │ │ 10 - 6 - Learning Curves (12 min).srt
7 @1 w0 j5 t, y9 O' D │ │ 11 - 2 - Error Analysis (13 min).mp4+ Z8 _/ _. a; \& q
│ │ 11 - 2 - Error Analysis (13 min).srt
1 i p. `3 u4 K# G. o& _ │ │ docs_slides_Lecture10.pdf
7 d. P$ \4 z* ^# d2 V, H │ │
' T/ Y! v# q) `3 @0 y │ ├─week 7
9 H& ^# e- L6 `% y │ │ 12 - 1 - Optimization Objective (15 min).mp4$ y, _9 t7 i8 k$ N( a8 w0 i! \
│ │ 12 - 1 - Optimization Objective (15 min).srt
2 a/ e1 o' ?" E' s/ Z- _3 { │ │ 12 - 2 - Large Margin Intuition (11 min).mp4& c# T( r- I9 K; a. h7 Z% z4 W
│ │ 12 - 2 - Large Margin Intuition (11 min).srt. j9 N# j, w1 q- Z. @
│ │ 12 - 4 - Kernels I (16 min).mp4
$ C: J# ~# i5 O0 a5 Q) i │ │ 12 - 4 - Kernels I (16 min).srt7 K( z- x- S. o9 e) E b+ f
│ │ 12 - 5 - Kernels II (16 min).mp4' l* F1 J! W( t, O$ l& R4 t
│ │ 12 - 5 - Kernels II (16 min).srt
% Y# B: D8 v& o1 Z │ │ 12 - 6 - Using An SVM (21 min).mp4
' }" u5 u" K$ X0 i* {' N │ │ 12 - 6 - Using An SVM (21 min).srt# R' r1 G$ O0 S0 Q
│ │ docs_slides_Lecture12.pdf* t0 G9 \, E2 X' s7 L
│ │ ) I4 j( c2 j5 z2 K, A
│ ├─week 8
S$ w- B/ p3 i1 G │ │ 13 - 2 - K-Means Algorithm (13 min).mp4+ s1 U$ ^ ~7 B* f. L& a5 m8 j
│ │ 13 - 2 - K-Means Algorithm (13 min).srt
, k' c! f! X# M ]& {6 H │ │ 13 - 3 - Optimization Objective (7 min).mp4
! ~: B( f- z. y4 z. P3 N+ F! C │ │ 13 - 3 - Optimization Objective (7 min).srt
) g, D8 @- B3 Y' _, \" [ │ │ 13 - 4 - Random Initialization (8 min).mp4
8 E5 _8 o, g" ~6 C& v& y, T2 ]6 b │ │ 13 - 4 - Random Initialization (8 min).srt+ v! { G* }/ ^3 y, d* [
│ │ 14 - 7 - Advice for Applying PCA (13 min).mp4" V8 r2 R* n) g/ j
│ │ 14 - 7 - Advice for Applying PCA (13 min).srt+ [* ]4 A; z. ~2 \5 o/ s0 A6 n8 _
│ │ docs_slides_Lecture13.pdf
: f4 c4 F6 T) ]8 e │ │ docs_slides_Lecture14.pdf
, H$ |$ T, d0 ~+ t+ |; x$ w( V │ │ 8 E) _% I) C" c7 i* K. v8 C [4 x
│ ├─week 9* I0 j k. A8 u) w' R: H( Z
│ │ 15 - 1 - Problem Motivation (8 min).mp4
8 q1 B+ y+ P7 z7 o+ `/ o │ │ 15 - 1 - Problem Motivation (8 min).srt
T* C( Z1 y1 a0 J5 T0 k5 R │ │ 15 - 2 - Gaussian Distribution (10 min).mp4% D% w3 N% G% k, k$ n
│ │ 15 - 2 - Gaussian Distribution (10 min).srt$ |" B* v0 a) Z6 } K( `7 \
│ │ 15 - 3 - Algorithm (12 min).mp4( q% k8 l2 E* g; ^
│ │ 15 - 3 - Algorithm (12 min).srt
4 D; M+ Q/ B& W0 o' }" G) w. h │ │ 16 - 1 - Problem Formulation (8 min).mp44 z; J' M* @; M% W7 l% Z! {. b
│ │ 16 - 1 - Problem Formulation (8 min).srt, X' N' U% W# e: |
│ │ 16 - 3 - Collaborative Filtering (10 min).mp4
" H4 w9 c M2 Y1 a3 D# r+ v" P* e │ │ 16 - 3 - Collaborative Filtering (10 min).srt' ^) @7 h0 L5 j5 A8 ?' \( {
│ │ docs_slides_Lecture15.pdf
; n) C/ |4 H+ ~$ g! @3 M0 D( q V │ │ docs_slides_Lecture16.pdf
8 ^! [& ^% l4 N6 G3 O │ │ 6 Y& ~7 z8 O: t$ S, n& f
│ └─作业
# v4 l( } _5 T. B+ p7 f% {1 b │ │ ex1.zip
* R4 }" i9 d, }; H │ │ ex3.zip+ j8 a/ |6 P- m9 b! F3 n1 e
│ │ ex4.zip
% [( M2 O w% W. A4 T9 H+ c9 o │ │ ex5.zip/ ~# N9 G, M' ]$ p) o' f9 R& h$ `6 k
│ │ ex6.zip
2 J, g1 o1 C- |7 K, f! c# R' B- p │ │ ex7.zip
# ]! ]5 N5 \( M1 W │ │ ex8.zip0 X1 g5 ^% y: g0 U* v. ^
│ │ Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe: F% Z% G$ R6 @* O3 S
│ │ 7 e' f" N# u ]) s2 [, i
│ └─答案; A' F) W% Y* l
│ ├─ex1
! S/ j# y; H# h │ │ computeCost.m
e/ S7 l* U# s! U │ │ computeCostMulti.m
$ |2 ?5 E. M' B+ S │ │ ex1.m& A5 D ~! z7 C3 x7 i9 x; n5 H+ |4 z
│ │ ex1data1.txt
$ V( ^. M+ T7 { │ │ ex1data2.txt- Z& V. q/ ~+ |6 R: ^' O
│ │ ex1_multi.m# ?/ U& ?+ ? b. C5 {: U! L. q+ R
│ │ featureNormalize.m
" O( q# I1 X% g0 \ │ │ gradientDescent.m! s+ Q8 m3 g% I9 m8 U
│ │ gradientDescentMulti.m
: |) |5 b0 S! ~( \( m │ │ ml_login_data.mat* r+ C2 |) a' t- h' d
│ │ normalEqn.m
! f, ?/ t& l# k" `, }- g6 [ │ │ plotData.m0 q: s* g3 J; e1 l3 g, e" g" M0 D
│ │ submit.m& n4 j0 c# ]2 s2 Y9 N& Z
│ │ submitWeb.m
* Y3 F5 _$ E. o- z │ │ warmUpExercise.m
; s$ |8 ^ ~' t- N' W7 X │ │ + A6 g# d# I4 c/ g0 H( R' g
│ ├─ex2
, j" ]' W" Y C; \ │ │ costFunction.m6 K$ |# R6 e0 M u1 S
│ │ costFunctionReg.m i6 R+ f; s. q/ A/ ~0 k# M& b( O( V
│ │ ex2.m% i% n1 h, n3 A) d2 `6 ?
│ │ ex2data1.txt
k0 A2 @0 h( s9 ?, t% R# i$ s# v │ │ ex2data2.txt& U( s' t# t9 `
│ │ ex2_reg.m
3 T. y8 [/ b) ^; o4 h9 c, N% j% k2 M │ │ mapFeature.m8 R% g* N/ T; t
│ │ ml_login_data.mat
B0 H7 v) y3 D* J) [9 v# U │ │ plotData.m
& ?$ e* C; z& Y+ c/ |' y. x │ │ plotDecisionBoundary.m
: u, }; _5 Q# i' A- X" i │ │ predict(1).m.baiduyun.downloading
5 W9 K: B5 ~+ \7 T4 D% U. u& Y! m │ │ predict.m* z( a( R& |3 I' {5 J- o
│ │ sigmoid.m
3 h$ O; j0 }* a7 y" F' g$ ^9 _ │ │ submit(1).m.baiduyun.downloading. J' e5 ~ r, _, @8 A
│ │ submit.m
2 K" ^ I) r! L6 b K9 H( j │ │ submitWeb(1).m.baiduyun.downloading
( \. @! m( H2 _4 n2 O3 I9 M7 s" ^ │ │ submitWeb.m
5 r$ W8 u$ ]- g; {0 {1 P │ │
& h& ], `% q, B- a2 ` │ ├─ex35 ~8 K) G# B S7 a7 R* x
│ │ displayData.m
6 e0 x. k2 K& ^7 `9 L% C │ │ ex3(437).m.baiduyun.downloading5 T, J% m/ r1 } d0 U
│ │ ex3.m8 n% h( [6 {+ G0 O+ i9 }
│ │ ex3data1.mat: S- o3 Z: h1 u/ Y; I3 _6 v- z
│ │ ex3weights.mat3 e$ G/ ~ s/ \. J! N
│ │ ex3_nn.m
8 C/ D7 L2 y+ j/ ^1 s │ │ fmincg.m
: {4 {8 c* J2 `; R8 r2 h' h' e │ │ lrCostFunction.m
: }+ U( Q9 Y; Z, Q9 J │ │ ml_login_data.mat
% r# z: |% m' v# g' D( y │ │ oneVsAll.m
! V- t3 S1 ?, o │ │ predict.m
0 `5 E2 P2 V7 y, p( } │ │ predictOneVsAll.m" r! _# N9 E# x" j
│ │ sigmoid.m8 a/ s; C8 t2 |6 z; _4 a$ `
│ │ submit.m& g l2 R* P& U
│ │ submitWeb.m
4 E1 S4 q' V6 _$ j" z │ │
9 ~+ L, P& n/ k8 h' l │ ├─ex4- ^3 @9 e5 z$ o7 [
│ │ checkNNGradients.m$ D B* l/ K6 v9 d+ ?; x
│ │ computeNumericalGradient.m! g/ \5 P% j* _
│ │ debugInitializeWeights.m
& h; T1 R1 } `1 j: c │ │ displayData.m+ r6 m$ b5 t( j0 U' f9 }% O% Q
│ │ ex4.m+ y. h$ j( u+ I5 T
│ │ ex4data1.mat! d4 ~" h8 o; g- ~5 w- R
│ │ ex4weights.mat2 k' ^& r3 ^( U; ?# o
│ │ fmincg.m
3 C# C8 ^4 g$ f+ X! U5 G5 Y+ o │ │ ml_login_data.mat
9 \, C4 \9 d) Q0 _$ L0 \9 W" U │ │ nnCostFunction.m' m# t1 O, e" }$ w7 d3 K
│ │ predict.m- R- E- @6 G: F# e( X9 _1 a
│ │ randInitializeWeights.m
: }) Q1 F7 G" {# r% n: \7 D+ W │ │ sigmoid.m8 C+ T7 ^" K- s+ J* a8 G+ `
│ │ sigmoidGradient.m0 Y* w# o' K7 Y0 P
│ │ submit.m
1 i& m2 s) G! }5 P7 J │ │ submitWeb.m" w2 b7 C6 Y6 v
│ │ 3 @. H. i* N5 B) L/ C0 h' M
│ ├─ex5
8 k! k* j4 O* T' E- E5 k │ │ ex5.m3 U+ ]6 w6 ^! ?& A
│ │ ex5data1.mat
, F. k" t* Y: a' }, t; N │ │ featureNormalize.m' U% T1 p) k8 f( k: j
│ │ fmincg.m
7 A |; c$ T9 U, C& w" q0 j7 V │ │ learningCurve.m
6 G3 q T0 a! D: m* x │ │ linearRegCostFunction.m# ~ z, w+ p, R, |& i0 P
│ │ ml_login_data.mat! d3 K5 F: Y# Y2 D
│ │ plotFit.m
' ^6 d ^% B6 L8 _ │ │ polyFeatures.m
' J( Q J: j* x. B2 r3 U X4 U │ │ submit.m
, u- d8 p( V3 ^6 P' A │ │ submitWeb.m1 X$ b% F6 M. Q/ m ^/ L5 g. O
│ │ trainLinearReg.m; e- x. R2 v1 Y) `9 W
│ │ validationCurve.m% P: S" Z5 i$ x9 {
│ │
! q; v! P5 K1 o& }+ @9 _) } │ ├─ex66 Y" O7 Z3 }. M+ F- X
│ │ dataset3Params.m
6 l/ v3 F0 s% t# e0 l2 h. ^4 _2 H │ │ emailFeatures.m
! Z: G# ~8 M W$ r │ │ emailSample1.txt
% V* X% h: B K( L* t; z$ M6 |! A* n │ │ emailSample2.txt
" m+ n/ w1 U7 _) K/ {; ` │ │ ex6.m+ z9 E! b d3 I6 ~& C: I# v! @
│ │ ex6data1.mat4 J' n F$ T4 j! m, J+ F
│ │ ex6data2.mat
" R3 `4 M% R2 k: E1 n │ │ ex6data3.mat. V5 j; U' g* J+ @, g" U- V/ p
│ │ ex6_spam.m3 S+ y8 S# J9 `* E2 r" r L
│ │ gaussianKernel.m
' k' _3 j/ b) _; T │ │ getVocabList.m0 d) G, }2 V3 ~9 ^
│ │ linearKernel.m0 B7 P2 q/ X& E" S- Q
│ │ ml_login_data.mat
( P! n9 B5 i: x t; o │ │ octave-core$ M# x' c9 E: M; Q- A
│ │ plotData.m
8 R3 p& O6 o# Y- [ │ │ porterStemmer.m
( v, T! i, g/ w0 }4 m+ q1 V │ │ processEmail.m" m) ]+ E) u8 g. d8 c
│ │ readFile.m4 p" I" c0 e% } `; x$ [
│ │ spamSample1.txt3 H8 @/ l5 \& _3 U, f1 H
│ │ spamSample2.txt. a" Y; N2 j; s. w2 t' z
│ │ spamTest.mat
$ U' o l, n C$ X1 x! O4 Z# e0 Z2 I │ │ spamTrain.mat
" | _+ Q: y3 j: }- ]$ q+ s │ │ submit.m; v7 x2 b! ?0 T
│ │ submitWeb.m3 b0 b3 k0 f; T" N
│ │ svmPredict.m! U3 U* u8 S6 w# s- B. G% c. ?
│ │ svmTrain.m* `2 t/ O+ M3 M7 a6 J; q
│ │ visualizeBoundary.m
" C# t. j0 r, Y │ │ visualizeBoundaryLinear.m. t$ k ?% F1 y- f. A9 s
│ │ vocab.txt/ l% w/ V# A1 l7 ~
│ │
& x0 w, g5 ~% k Z9 U, y │ ├─ex79 Q S) T: l# b
│ │ bird_small.mat" T9 `2 ` i# K" O/ J& Q
│ │ bird_small.png
7 o! l: Z) ` u# x! F( A" n# `0 \ │ │ computeCentroids.m
' |! A5 \3 O; p9 }9 U% @) I │ │ displayData.m
3 I" R+ R/ X! d& n │ │ drawLine.m8 W: E: t! O7 V, n3 y7 b
│ │ ex7.m u. V) l( l9 m* R9 V- t
│ │ ex7data1.mat
1 V5 O, W. N' S8 b( v0 d+ x │ │ ex7data2.mat
! S$ d; _$ `; K) [ │ │ ex7faces.mat" p) _3 a+ N( q/ b, M+ l
│ │ ex7_pca.m
* @0 {. [0 I G$ j3 J- _4 o │ │ featureNormalize.m
}! e E& ]1 i6 B/ a │ │ findClosestCentroids.m
; @/ D6 B& i7 }+ a$ Y │ │ kMeansInitCentroids.m& g& D0 Y; q7 V9 ]5 z; f% {
│ │ ml_login_data.mat
+ C8 ?& ^; p6 }0 m │ │ octave-core
+ u3 o, i8 z1 n# j │ │ pca.m: d; F7 b& B, j! z# A
│ │ plotDataPoints.m2 r+ I* w3 i3 d1 ~. X% k1 l6 Y$ a6 W
│ │ plotProgresskMeans.m
4 v! f4 P% i1 d1 A! c& }+ d5 G+ L, f │ │ projectData.m- P \. L7 M. G5 V4 `
│ │ recoverData.m
. A8 m9 [. N, d s+ A# n │ │ runkMeans.m
# Y3 q0 m% A- i1 P: o1 a2 K │ │ submit.m
$ ^9 u. V# g2 J! u) X2 @0 { │ │ submitWeb.m8 |$ l6 [0 q; v6 F( ^/ O% _; |
│ │ 4 a; s4 f% D$ a3 @; d5 e
│ └─ex8
8 P: ]6 Z$ q0 V; ^( Z. K. s │ checkCostFunction.m8 ^! J8 ^3 o+ {7 A) C9 P/ G
│ cofiCostFunc.m
, r. u* P( G( Y V; [ \ │ computeNumericalGradient.m
. }: h k2 y7 D │ estimateGaussian.m+ }: z2 P$ p3 p6 ~: U
│ ex8.m
: U5 a1 u, H. L/ j │ ex8data1.mat- r7 k7 G7 d' j% x+ q
│ ex8data2.mat$ M+ q3 P2 f) H/ c( }
│ ex8_cofi.m% H, \0 H' i- p# H
│ ex8_movieParams.mat
7 s. v0 T7 x, l2 e: B │ ex8_movies.mat' n x _1 e) r! X; J
│ fmincg.m8 P- Z/ `4 o# Y r' w6 J5 p
│ loadMovieList.m
O S! E- H ?+ O) {% U │ ml_login_data.mat, p6 M% H7 `$ p7 t1 u! r( c/ o; Y% c% |1 a
│ movie_ids.txt# Z# j! ~% a9 e, b; \5 i5 k
│ multivariateGaussian.m. Z2 F& V. M n8 f+ p. X3 A
│ normalizeRatings.m. |/ K$ O, z4 U0 J0 p
│ selectThreshold.m
y" u* t% F. Q2 Z! X9 O5 Y │ submit.m
1 u) ?2 x% p. }5 M7 G: E0 P │ submitWeb.m
# ]$ F5 C0 g( M( @$ e* i6 q6 C │ visualizeFit.m
/ s7 ~& Q6 ^9 w9 Y; c* N3 k* V( q" I │ 9 J9 v( ?, p5 y$ X, Z' m: D- X
├─003_机器学习基石
% `. U3 L: B. p* {! [% l- X │ └─003_机器学习基石4 r6 O+ K( O" F0 Q3 j# Q( ?5 I
│ │ 下载说明.txt3 J6 p6 F8 j) N1 r* G7 F; y
│ │ 关注我们.png
* l/ b, R% E5 K8 B- ? │ │ 攻城狮论坛=网络技术+编程视频.url3 C% M) T1 e( e3 g. m! s) |9 w
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
) i' d/ U1 x/ c7 r1 | │ │ ' J9 R0 t# R3 ]0 L) o
│ ├─Machine Leaning Foundations - TNU2 V& X. j# Z) d8 O1 U3 H! X
│ │ 01_handout.pdf
3 `% k$ k4 ~ B; T │ │ 02_handout.pdf
2 a" i8 D, j5 E& H6 V; L │ │ 03_handout.pdf
$ i2 T# g1 I8 \4 y) g" C3 W3 L8 q │ │ 04_handout.pdf7 m- i e5 x j. ?$ u
│ │ 05_handout.pdf# J2 j& I. Y4 U
│ │ 06_handout.pdf
4 ?5 C; U8 Q: T" v3 l1 A1 {' f │ │ 07_handout.pdf
+ Q z- k( j1 D+ K$ q │ │ 08_handout.pdf) \' \( E$ b/ R
│ │ 09_handout.pdf2 c6 P, b% z: U- O. ^8 ~
│ │ 1 - 1 - Course Introduction (10-58).mp4
' N5 E% h9 M9 p% Y │ │ 1 - 2 - What is Machine Learning (18-28).mp4
4 e' z+ X, i/ @8 v; B) a2 }+ ^ │ │ 1 - 3 - Applications of Machine Learning (18-56).mp4 d$ w% ]) V; S% o& d6 I3 d4 r1 J9 g- |
│ │ 1 - 4 - Components of Machine Learning (11-45).mp4
9 c5 U6 ^5 C3 K7 F7 i; D │ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp45 ~+ G4 d! j0 e* w: [. ?% A1 E
│ │ 10 - 1 - Logistic Regression Problem (14-33).mp4. J1 Z& m0 W8 ~, O( ^* i; I# g
│ │ 10 - 2 - Logistic Regression Error (15-58).mp4" Z5 J( ]. ?. }. m% ]
│ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4
6 @, p" m" v& \! G: ~ H │ │ 10 - 4 - Gradient Descent (19-18).mp4
$ X- S d! a3 `& R% e! j! H │ │ 10_handout.pdf
" N5 v* r9 _, t8 f3 i+ ]% y& R │ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4" g* i, c+ e! x! T' Y
│ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4
8 r5 Y5 i, k& _3 I T │ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp4
$ e: M0 n# O8 V% A4 |" D │ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4, Z& Y6 y \, Q S l
│ │ 11_handout.pdf
/ z+ u! K! l; U7 F; c: t │ │ 12 - 1 - Quadratic Hypothesis (23-47).mp4" r, V' X i$ i
│ │ 12 - 2 - Nonlinear Transform (09-52).mp4
& L# g1 r6 b d6 M7 o# l+ D │ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp4
7 v9 p7 e) O Y, g │ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4% h. |# R/ ~- c4 E! U
│ │ 12_handout.pdf
: K m" Q# Q$ P* Y' u │ │ 13 - 1 - What is Overfitting- (10-45).mp4
' S1 R7 S$ P1 `! p) U │ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4/ m3 Q4 k6 N5 J7 G2 x7 q/ j
│ │ 13 - 3 - Deterministic Noise (14-07).mp46 ?( Z" F4 `' A+ h
│ │ 13 - 4 - Dealing with Overfitting (10-49).mp4
% T5 H0 P8 Z( r │ │ 13_handout.pdf
3 y) |9 u* U4 Q# F& X: ^/ Y │ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp41 l& J) D& J% z% M' V
│ │ 14 - 2 - Weight Decay Regularization (24-08).mp4
4 H3 [7 s. u. K; t2 r! ~+ U7 B │ │ 14 - 3 - Regularization and VC Theory (08-15).mp4" I* ^4 C' X3 e8 h U* N/ A1 i- L2 F4 T
│ │ 14 - 4 - General Regularizers (13-28).mp4" X. l2 {& z+ o @6 p
│ │ 14_handout.pdf
% S$ y& X) t3 J │ │ 15 - 1 - Model Selection Problem (16-00).mp40 [7 w$ F$ P% b
│ │ 15 - 2 - Validation (13-24).mp4( E1 T7 K/ |& M% k+ ~
│ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp42 Q& o% n# Q4 a
│ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4
3 G) i# G! P) R9 K3 v) s4 D2 B- Q │ │ 15_handout.pdf. x1 H; a9 s" U. [: \2 N1 F! B* s( x
│ │ 16 - 1 - Occam-'s Razor (10-08).mp4
9 Q9 u- G5 F" q. V- ~+ B: w" B │ │ 16 - 2 - Sampling Bias (11-50).mp4
0 _, C2 E& i! J │ │ 16 - 3 - Data Snooping (12-28).mp48 D- L4 g0 a( f7 H" _
│ │ 16 - 4 - Power of Three (08-49).mp4
9 C1 h5 H D Y$ T' D- S │ │ 16_handout.pdf% {+ b$ [) k, L: f I( C
│ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4! Y: H6 H0 Y, t. K' T6 \
│ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp44 [" I% L& x- ?8 x3 k; H8 a
│ │ 2 - 3 - Guarantee of PLA (12-37).mp4+ `- P; m+ t3 @6 @! R
│ │ 2 - 4 - Non-Separable Data (12-55).mp4
' _$ e$ x2 G! d( n: d │ │ 3 - 1 - Learning with Different Output Space (17-26).mp4/ e$ n6 l s* }9 e* I" F) R P5 O8 }
│ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
' s8 r. w4 U; t/ _/ D* F │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4; ]; ], s3 i- ?& g I' c: ?, ^' Y
│ │ 3 - 4 - Learning with Different Input Space (14-13).mp4
1 I# }+ Y. E7 p; E: D& H* g1 r( b │ │ 4 - 1 - Learning is Impossible- (13-32).mp4: u8 b1 O: R( r" C! j9 c
│ │ 4 - 2 - Probability to the Rescue (11-33).mp4
: H" p1 R1 i5 f. p9 O │ │ 4 - 3 - Connection to Learning (16-46).mp4
; l* ]8 P' B Q% J' P │ │ 4 - 4 - Connection to Real Learning (18-06).mp4
' x3 q9 Y, z7 _' N" p- S4 @' @" _ │ │ 5 - 1 - Recap and Preview (13-44).mp4
' C9 |2 C7 a# |2 L$ x │ │ 5 - 2 - Effective Number of Lines (15-26).mp4/ }0 K9 P! o3 B1 b2 @
│ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp46 K3 L$ P' t: P. K/ Z
│ │ 5 - 4 - Break Point (07-44).mp4/ z! r4 ?1 P0 R
│ │ 6 - 1 - Restriction of Break Point (14-18).mp4
) W S! z" o$ t9 M │ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4
" b4 A# d% r7 u% _( a- g4 f │ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
) e' N8 o6 m& @# f3 c6 {7 s │ │ 6 - 4 - A Pictorial Proof (16-01).mp4' G9 Q+ Y5 s+ S) B: |9 i
│ │ 7 - 1 - Definition of VC Dimension (13-10).mp4
8 o& `% e# b5 ~) b: B5 k6 T │ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp4
$ ?* w' e9 c. j5 \ u9 _ │ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4
5 \( E8 f: _: s* O │ │ 7 - 4 - Interpreting VC Dimension (17-13).mp4
% V6 ~# j. l! s) e" A, c │ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4
) o5 A1 V: G& h │ │ 8 - 2 - Error Measure (15-10).mp4* X% q$ H2 p8 o4 j( t9 S" x
│ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4
% Z& P5 _2 w$ O" E4 s: O │ │ 8 - 4 - Weighted Classification (16-54).mp4( V8 _7 C' d9 u0 F- [) ~4 ~2 e4 z
│ │ 9 - 1 - Linear Regression Problem (10-08).mp4" c A8 b4 S8 A5 W% n
│ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4# Q5 V- k9 r' p5 S
│ │ 9 - 3 - Generalization Issue (20-34).mp4
8 O; U8 P& x4 m │ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp4) y4 O' C; b/ E* u) r/ M
│ │ / q- ?; P- a. H2 s. M) x+ c
│ ├─机器学习的基石
6 h. J5 h7 v. ?1 X1 m; k │ │ 1 - 1 - Course Introduction (10-58).mp4
6 Q7 z& A* @+ W' x% m5 T │ │ 1 - 2 - What is Machine Learning (18-28).mp4
* ?" O6 v0 _! \% ?8 j( u │ │ 1 - 3 - Applications of Machine Learning (18-56).mp4
5 @1 h" u1 w: E. Z) R* M │ │ 1 - 4 - Components of Machine Learning (11-45).mp44 p' m, s) U( W* Z1 i% Z0 N
│ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4
: J8 g D }8 [ │ │ 10 - 1 - Logistic Regression Problem (14-33).mp4, L) C) s) m+ X/ n# i' k$ W
│ │ 10 - 2 - Logistic Regression Error (15-58).mp4" u, b' g5 N6 j# N
│ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4- X: M( a) q% m! u2 f. q$ I
│ │ 10 - 4 - Gradient Descent (19-18).mp4 p. S- _9 C/ |
│ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4. p+ \2 r! @ B5 F- w& l
│ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4' }. ^5 X, {" k: ]" y3 {+ k1 g
│ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp4+ z5 L' [0 o( j# n
│ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4
. k' j: M' t; B; Y" w1 _ │ │ 12 - 1 - Quadratic Hypothesis (23-47).mp4
& {* X' l2 C; [2 D- k! y. @; V │ │ 12 - 2 - Nonlinear Transform (09-52).mp47 U- G) D4 ]7 `- F
│ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp4
7 o8 `/ a. F, S3 j" B- q2 a" Q │ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp46 g, i# h& G/ M5 k
│ │ 13 - 1 - What is Overfitting- (10-45).mp4+ M0 j( j$ ^5 @7 D. L
│ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4; y7 M. C) @6 `. w- ?0 X' D
│ │ 13 - 3 - Deterministic Noise (14-07).mp4& g" _2 D9 r, E r; I
│ │ 13 - 4 - Dealing with Overfitting (10-49).mp4( s) b# R s% W, ^; S( f' P
│ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4- W$ g. Q) h* ]' f
│ │ 14 - 2 - Weight Decay Regularization (24-08).mp42 V. c$ R# ?) c. W) N
│ │ 14 - 3 - Regularization and VC Theory (08-15).mp4
) D6 F1 L. E4 g; O2 P │ │ 14 - 4 - General Regularizers (13-28).mp4; Z9 Y6 K2 H4 ?( A: N. u
│ │ 15 - 1 - Model Selection Problem (16-00).mp4
' g: P: f+ ^: O; N3 O1 [( G │ │ 15 - 2 - Validation (13-24).mp4
0 Q$ g) c0 |/ b1 o6 \ │ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
( {' R" ^- u0 P. s5 Q │ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4 W$ ?' _, P: x9 x0 s; o" x
│ │ 16 - 1 - Occam-'s Razor (10-08).mp4* j: h5 L8 C; y9 h" P7 A+ V$ j
│ │ 16 - 2 - Sampling Bias (11-50).mp4
8 b: j) \& F7 j4 l9 M4 P │ │ 16 - 3 - Data Snooping (12-28).mp4
. a$ n; v5 l: r G9 s; r! c │ │ 16 - 4 - Power of Three (08-49).mp4
0 e' O! F/ p, K │ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4
$ W/ v6 A: y+ a o2 ? I6 C │ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp45 H+ B/ a6 Z3 n* Z
│ │ 2 - 3 - Guarantee of PLA (12-37).mp4# a1 U9 V T; R+ F/ P
│ │ 2 - 4 - Non-Separable Data (12-55).mp44 m( e7 ?( ]0 b7 X+ b6 R. k* T
│ │ 3 - 1 - Learning with Different Output Space (17-26).mp4' w3 l. }" F0 P: `# s
│ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
8 k7 G1 _1 W% r2 v# q: f: V │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4
0 @5 a3 j4 i2 o) t, }( O, V. S │ │ 3 - 4 - Learning with Different Input Space (14-13).mp41 N$ N/ n- X9 w+ j6 s" D. Q
│ │ 4 - 1 - Learning is Impossible- (13-32).mp4
2 {& z9 `. M, ^5 c8 y │ │ 4 - 2 - Probability to the Rescue (11-33).mp4
7 @1 F6 a% \ H5 P0 A: [ │ │ 4 - 3 - Connection to Learning (16-46).mp4- G) U1 f/ G4 M" A4 V
│ │ 4 - 4 - Connection to Real Learning (18-06).mp4, k- I* C: _$ k2 q& A5 j
│ │ 5 - 1 - Recap and Preview (13-44).mp4: W& y, B" E6 \! B
│ │ 5 - 2 - Effective Number of Lines (15-26).mp46 o9 X+ S9 V" J8 R k% S
│ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp4
' D [" p; n7 B4 T8 [+ e │ │ 5 - 4 - Break Point (07-44).mp4
$ `3 w* v$ C$ b0 ]- G │ │ 6 - 1 - Restriction of Break Point (14-18).mp4; ^! A- b5 u7 m; [
│ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4
, t1 G" }$ `+ B" c P$ a/ s │ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4) c) _7 j: N# w' h# {" }9 g
│ │ 6 - 4 - A Pictorial Proof (16-01).mp4
6 `, ]) b o2 q8 z4 r$ a7 v( j) k% N │ │ 7 - 1 - Definition of VC Dimension (13-10).mp4
1 I! e5 I7 Q& @: }# D u; r& v │ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp47 d9 M- C/ R. s" O; y) d9 G& M
│ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4
$ f# v x7 o2 L │ │ 7 - 4 - Interpreting VC Dimension (17-13).mp4( K- d6 ~$ s g0 @' i
│ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp47 o' j1 h% j$ F8 j0 Y8 ~" @( V
│ │ 8 - 2 - Error Measure (15-10).mp4
5 {1 G) p7 O0 u# g( }1 f │ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4# \ n' P9 R* k; m, T# v, Q
│ │ 8 - 4 - Weighted Classification (16-54).mp4' ]9 r$ f( d" C- x \, V
│ │ 9 - 1 - Linear Regression Problem (10-08).mp4% D# R) J$ L o3 J
│ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4
8 S8 n; J/ n' [. M* V │ │ 9 - 3 - Generalization Issue (20-34).mp4
/ @8 k. L; j4 V' I+ `* j5 c │ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp46 D* P/ K/ {# I0 O
│ │ lecture_slides-01_handout.pdf
/ L! u1 y1 b, \( F. | │ │ lecture_slides-02_handout.pdf3 b$ W0 G# S2 k) f, b
│ │ lecture_slides-03_handout.pdf, F/ E( ^% [/ B3 f) u
│ │ lecture_slides-04_handout.pdf5 U: o$ y) a& h0 H, Y9 J
│ │ lecture_slides-05_handout.pdf3 W# @ v0 D4 B5 ~( f8 ^- Q' L
│ │ lecture_slides-06_handout.pdf) [3 o; |: J! H6 K* w! V
│ │ lecture_slides-07_handout.pdf- k2 o, P' S0 x3 O+ i$ z
│ │ lecture_slides-08_handout.pdf0 Z' ]- j( W5 k( X$ q
│ │ lecture_slides-09_handout.pdf
. x% S& M( @; w+ Z) t) g7 N │ │ lecture_slides-10_handout.pdf {9 D; g+ L, j3 O
│ │ lecture_slides-11_handout.pdf
8 H$ k3 P- t( v8 Z │ │ lecture_slides-12_handout.pdf; T+ u( q9 G7 I+ D% a% a; [4 s9 S
│ │ lecture_slides-13_handout.pdf
) V3 M3 l/ l( e │ │ lecture_slides-14_handout.pdf
- h, y1 }. A$ j$ l( z! H │ │ lecture_slides-15_handout.pdf. `8 ~# S7 o/ {3 H7 f/ P: \$ P& t
│ │ lecture_slides-16_handout.pdf9 g' U- ]: X" z+ b m% K* Q7 H
│ │
* s @9 H- k- I% D8 `% W9 M& E% p │ └─機器學習基石4 O. ?, |+ M8 Y: e
│ ├─01_-_The_Learning_Problem# }4 I$ I/ U b( d" V
│ │ 01_Course_Introduction_10-58.mp49 ~# ^; R$ E! x+ }! L E' w
│ │ 01_Course_Introduction_10-58.pdf4 k, Q+ L' K$ J2 z F2 L/ }6 T
│ │ 02_What_is_Machine_Learning_18-28.mp4
3 O" \/ B( |% H/ k( l$ q) ~ │ │ 03_Applications_of_Machine_Learning_18-56.mp49 X" g% e. i7 B& J
│ │ 04_Components_of_Machine_Learning_11-45.mp4
! G! p, m9 {1 n, ?6 x. g │ │ 05_Machine_Learning_and_Other_Fields_10-21.mp41 B. p, K5 B3 {1 [
│ │ " m S! }$ [* ^# t8 H
│ ├─02_-_Learning_to_Answer_Yes-No* u" Z3 p8 {( [( e* b; t: a
│ │ 01_Perceptron_Hypothesis_Set_15-42.mp42 z$ b8 |6 N: L, s$ z0 T& E
│ │ 01_Perceptron_Hypothesis_Set_15-42.pdf
; F- z9 K' m+ O$ J- |6 k0 H& } │ │ 02_Perceptron_Learning_Algorithm_PLA_19-46.mp4% S9 G, G: }, A7 D
│ │ 03_Guarantee_of_PLA_12-37.mp4" X1 E' q* r, r F+ f( f
│ │ 04_Non-Separable_Data_12-55.mp4$ N( G# |' D6 z$ W9 |
│ │
$ i: N: s/ x# E. M! {/ B( v, M │ ├─03_-_Types_of_Learning
& e6 i7 [: n( ]" K) u4 p0 ? │ │ 01_Learning_with_Different_Output_Space_17-26.mp4
1 S$ H3 P" y$ h# h8 t6 { │ │ 01_Learning_with_Different_Output_Space_17-26.pdf
9 Z$ r& x% p4 W& `& h │ │ 02_Learning_with_Different_Data_Label_18-12.mp4- v* P% g6 s3 U2 u
│ │ 03_Learning_with_Different_Protocol_11-09.mp4
6 _, P4 C5 A) ^ │ │ 04_Learning_with_Different_Input_Space_14-13.mp4
H9 \* ]9 {% U$ B% A │ │
+ X6 g' G5 d$ ?( O/ P: @* _: { │ ├─04_-_Feasibility_of_Learning3 u; ^8 E! i( {% q8 x* c, @. u
│ │ 01_Learning_is_Impossible_13-32.mp47 q6 g( F: G c3 B
│ │ 01_Learning_is_Impossible_13-32.pdf; e$ N! S! L% R
│ │ 02_Probability_to_the_Rescue_11-33.mp4/ U- w( ?5 j/ W/ z/ E4 n1 t( u$ Z# P
│ │ 03_Connection_to_Learning_16-46.mp43 n. l; k7 `. F# f1 u+ i7 ^
│ │ 04_Connection_to_Real_Learning_18-06.mp4
1 ?( a8 t: ]7 v) N3 t │ │ 7 S# l: b) h7 S% k
│ ├─05_-_Training_versus_Testing
/ J; n' R7 N7 P& W( D; j) i c6 a │ │ 01_Recap_and_Preview_13-44.mp41 E; T) j$ i3 e: N" Q
│ │ 01_Recap_and_Preview_13-44.pdf
- j$ o7 j) p' G) m5 b# M │ │ 02_Effective_Number_of_Lines_15-26.mp46 Q3 o8 i& ~$ r! W
│ │ 03_Effective_Number_of_Hypotheses_16-17.mp4
3 W5 I; U: M6 k: _7 l* M │ │ 04_Break_Point_07-44.mp4
- @1 P5 }) F1 N# N$ ~ L │ │
+ o+ q( t: U( b w4 t$ o │ ├─06_-_Theory_of_Generalization
. A( q$ C E; {5 b2 s7 W0 o │ │ 01_Restriction_of_Break_Point_14-18.mp4* }2 B2 y" d8 a j% T, a O6 r
│ │ 01_Restriction_of_Break_Point_14-18.pdf8 O+ ]8 a( T2 x8 y) e w* W
│ │ 02_Bounding_Function-_Basic_Cases_06-56.mp4
) J! j1 C7 W3 D) Y │ │ 03_Bounding_Function-_Inductive_Cases_14-47.mp4# i% R! v- X8 F2 c
│ │ 04_A_Pictorial_Proof_16-01.mp4
5 u- x. [2 ]0 P+ S9 E F2 ?1 g' m │ │ 0 Q& h# b) P* V7 [; i8 l7 S9 O
│ ├─07_-_The_VC_Dimension
8 ?- ~( z3 h. ?( z │ │ 01_Definition_of_VC_Dimension_13-10.mp4. d: {0 c/ T3 j) R5 R- z
│ │ 01_Definition_of_VC_Dimension_13-10.pdf6 X' c/ G& v0 p, C' Q
│ │ 02_VC_Dimension_of_Perceptrons_13-27.mp4
4 N0 s7 R2 R6 i: H; j1 _# C │ │ 03_Physical_Intuition_of_VC_Dimension_6-11.mp4
: Y4 w' {' Z) ]: c │ │ 04_Interpreting_VC_Dimension_17-13.mp4
9 V# _5 F3 D9 N5 ]& L7 v │ │ : w) H" \+ i5 t9 R) h m
│ ├─08_-_Noise_and_Error
0 L r: w! O& Y8 A │ │ 01_Noise_and_Probabilistic_Target_17-01.mp4
# h7 T1 {+ j+ K' @& i) W5 M$ c │ │ 01_Noise_and_Probabilistic_Target_17-01.pdf
* x5 b& |& `- d% K9 ]& G9 y │ │ 02_Error_Measure_15-10.mp43 i* h4 e# ]& k
│ │ 03_Algorithmic_Error_Measure_13-46.mp4
8 b* L0 v: y, ^1 _ │ │ 04_Weighted_Classification_16-54.mp4
, a3 ^9 q5 W. v) }; a+ _0 p │ │ * H- G6 @0 R+ t4 L. E! U0 o/ v6 U
│ ├─09_-_Linear_Regression0 t2 o* n: G! } _- w3 Y
│ │ 01_Linear_Regression_Problem_10-08.mp4
1 K1 [! C$ D8 a9 m! e; \4 ~ │ │ 01_Linear_Regression_Problem_10-08.pdf
7 y7 L# \ M n. s, \7 Y# t │ │ 02_Linear_Regression_Algorithm_20-03.mp4
. [6 I* e: N3 t4 o. h2 p │ │ 03_Generalization_Issue_20-34.mp4
( S0 i4 w2 i- C# ?+ d │ │ 04_Linear_Regression_for_Binary_Classification_11-23.mp4
( |0 ~4 Y% s% L6 D │ │
8 s8 z4 s6 h- G7 B& \ w. o; [ │ ├─10_-_Logistic_Regression
; h) L T" `# {+ U: u- s- K │ │ 01_Logistic_Regression_Problem_14-33.mp4- s# Q* A2 Z7 v( x- F9 A1 R
│ │ 01_Logistic_Regression_Problem_14-33.pdf
$ w! k7 l/ p' G9 g │ │ 02_Logistic_Regression_Error_15-58.mp47 U+ g( x$ k* s5 [' N5 K, ]
│ │ 03_Gradient_of_Logistic_Regression_Error_15-38.mp4$ \2 l) W" w8 W# q" O
│ │ 04_Gradient_Descent_19-18.mp42 Y4 K& L) q/ N8 T2 s8 ~
│ │
3 H5 c4 R# Q) f* n; A │ ├─11_-_Linear_Models_for_Classification
* ^2 ~4 E; J# X │ │ 01_Linear_Models_for_Binary_Classification_21-35.mp4
+ [/ R9 J- W6 V- [: Z0 M │ │ 01_Linear_Models_for_Binary_Classification_21-35.pdf
, X* o% P I a" M ^1 S0 {% y │ │ 02_Stochastic_Gradient_Descent_11-39.mp4! X5 A; H3 d7 ~! ^) H& |
│ │ 03_Multiclass_via_Logistic_Regression_14-18.mp45 x r) e- K/ u6 {7 V1 d, I
│ │ 04_Multiclass_via_Binary_Classification_11-35.mp4
p$ e7 ?2 Q" Q │ │ H' b8 E1 K g7 H) G2 v& R1 ]
│ ├─12_-_Nonlinear_Transformation
& y: @3 L$ Z# G. Q │ │ 01_Quadratic_Hypothesis_23-47.mp4
: [* A: ~% Z3 B3 i1 d2 W │ │ 01_Quadratic_Hypothesis_23-47.pdf
6 L4 j( n H$ l [4 D9 O2 o │ │ 02_Nonlinear_Transform_09-52.mp48 g( ^. a$ K+ o) a$ Q# X) n4 s3 f
│ │ 03_Price_of_Nonlinear_Transform_15-37.mp4
. k9 H7 |3 O3 g/ {1 S │ │ 04_Structured_Hypothesis_Sets_09-36.mp4
a; `9 Q, n. C- H4 Z' `9 w2 b │ │ & g0 k d, x D4 J1 Q$ R: p" A
│ ├─13_-_Hazard_of_Overfitting
7 j/ O9 m5 w" \& m │ │ 01_What_is_Overfitting_10-45.mp4
+ ~7 E( ]2 {3 i3 A5 k* G │ │ 01_What_is_Overfitting_10-45.pdf B7 R0 m* F" Z# ]8 a
│ │ 02_The_Role_of_Noise_and_Data_Size_13-36.mp4
$ E* m# b F& V" A/ O5 @ │ │ 03_Deterministic_Noise_14-07.mp49 e7 r B+ D* J7 E Q. I. M0 m
│ │ 04_Dealing_with_Overfitting_10-49.mp4
/ O, v9 o% i- |# S) L │ │ - @% e2 r! {1 ?4 ?& I8 l. s% J- ^
│ ├─14_-_Regularization
6 a% v& F* ~9 j* s. { │ │ 01_Regularized_Hypothesis_Set_19-16.mp4 ~: K6 H% x) o% y
│ │ 01_Regularized_Hypothesis_Set_19-16.pdf* f e# S% e9 }8 P" h8 e& _ t
│ │ 02_Weight_Decay_Regularization_24-08.mp49 m6 `2 z4 V. q9 n% \
│ │ 03_Regularization_and_VC_Theory_08-15.mp4
1 M/ a% H; M& l9 v, F- } │ │ 04_General_Regularizers_13-28.mp49 o q( ^. k* K+ d+ t/ i C
│ │ : G, y j2 K" S% G. _$ q2 z. N
│ ├─15_-_Validation6 P0 ?5 `0 a+ f" G. z
│ │ 01_Model_Selection_Problem_16-00(437).mp4" G7 y: P8 c: ]/ i
│ │ 01_Model_Selection_Problem_16-00.pdf
1 s( D* N, l5 Y& P) o( A7 E8 _ │ │ 02_Validation_13-24.mp4
" R; |. N% K# O4 u3 w8 H, n │ │ 03_Leave-One-Out_Cross_Validation_16-06.mp4
/ K r, i p4 _* d; P! v │ │ 04_V-Fold_Cross_Validation_10-41.mp4. F5 _$ @, I R; W; n
│ │
& K( [# d1 y: }0 z1 C │ └─16_-_Three_Learning_Principles0 B, J2 i0 l4 v: P8 Y
│ 01_Occams_Razor_10-08.mp48 B, j5 }1 ~7 c# a/ h
│ 01_Occams_Razor_10-08.pdf( V+ s/ K/ G- H; z3 W
│ 02_Sampling_Bias_11-50.mp4
1 N- X8 K1 M' B │ 03_Data_Snooping_12-28.mp4
7 q, b6 q, k1 N$ U3 e8 ^/ t8 ?( x │ 04_Power_of_Three_08-49.mp4
[/ ^, f5 ^6 H$ R4 w5 L: y │ 6 |0 V' q& m) S/ a
├─004_机器学习技法. {7 T$ M0 o/ K) u/ S% R) V/ X* V
│ └─004_机器学习技法! w5 w: f" X( |2 F
│ └─機器學習技法
& ^# A9 m9 ?% W5 _0 L$ ~( `* J │ │ 下载说明.txt
2 K) N" P$ V8 V" U4 r, \9 Y │ │ 关注我们.png3 s& m' z( f5 P5 U
│ │ 攻城狮论坛=网络技术+编程视频.url
! y4 V! n0 A2 m% _: `& {, C │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt& }6 b6 p, s# _: B8 |
│ │ ' e. m% Y- B" m" [
│ ├─01_Linear_Support_Vector_Machine
$ \+ ]6 R' M3 |' y/ U │ │ 01_Course_Introduction_4-07.mp4
9 ?6 F5 \( m7 |9 `" J. l │ │ 01_Course_Introduction_4-07.pdf4 M2 X# c. Q: ]- l1 M
│ │ 02_Large-Margin_Separating_Hyperplane_14-17.mp4; f+ ~6 V/ H0 F0 ^. S+ k2 a" _0 N
│ │ 03_Standard_Large-Margin_Problem_19-16.mp4
/ c* E1 M; c2 f( o* }! c │ │ 04_Support_Vector_Machine_15-33.mp4
# L6 S: G& _3 e9 t( d* m% L │ │ 05_Reasons_behind_Large-Margin_Hyperplane_13-31.mp4( n4 Q6 ?4 k6 S9 L5 S& \/ V
│ │ ( p7 P( F [: O
│ ├─02_Dual_Support_Vector_Machine
( q9 }. t2 [0 H8 ^9 |$ u L │ │ 01_Motivation_of_Dual_SVM_15-54.mp4
P$ {8 n1 g7 w/ k: R │ │ 01_Motivation_of_Dual_SVM_15-54.pdf
5 ~5 `1 m: f8 X7 |* L │ │ 02_Lagrange_Dual_SVM_18-50.mp40 z' C* E7 ~, Z* U
│ │ 03_Solving_Dual_SVM_14-19.mp40 f% ^! @* Z% O3 s1 u- B; |
│ │ 04_Messages_behind_Dual_SVM_11-18.mp4( D+ V" l8 U8 i/ s$ [! m# o* t
│ │
3 h) K3 Y# _/ m0 ]. X1 S │ ├─03_Kernel_Support_Vector_Machine
[! F, w9 E- U- V │ │ 01_Kernel_Trick_20-23.mp4
3 F# @. [% H2 P │ │ 01_Kernel_Trick_20-23.pdf
; k/ c% T0 n- m3 V7 A! N │ │ 02_Polynomial_Kernel_12-16.mp4: n* V* K4 F0 O; c& r* r0 ?
│ │ 03_Gaussian_Kernel_14-43.mp43 [9 U+ d9 C/ F: B. J
│ │ 04_Comparison_of_Kernels_13-35.mp4. {# P/ z1 T! s) v5 K- }' G
│ │ 0 d. X- A- c* P; F
│ ├─04_Soft-Margin_Support_Vector_Machine) Y; D4 D) w: U; P
│ │ 01_Motivation_and_Primal_Problem_14-27.mp4, M. L; M) T4 ], n' V) ?" z2 n
│ │ 01_Motivation_and_Primal_Problem_14-27.pdf. f: l% R J5 @0 @5 O6 \
│ │ 02_Dual_Problem_7-38.mp4
3 W; W/ G( Q1 f7 ]& @3 q# ~0 H │ │ 03_Messages_behind_Soft-Margin_SVM_13-44.mp48 _7 Z2 e3 N/ m4 X3 @7 ] {
│ │ 04_Model_Selection_9-57.mp4, o/ g0 X- X1 [
│ │
1 S/ D( u Z8 O P" y% ~) d& C+ Y$ | │ ├─05_Kernel_Logistic_Regression
# J0 x+ c) z( n. } │ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.mp4. i( `- x( g2 U; l0 q
│ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.pdf
8 N# l' z! |; h' S+ x │ │ 02_SVM_versus_Logistic_Regression_10-18.mp4
4 T, _! n. {4 u' d1 S; T │ │ 03_SVM_for_Soft_Binary_Classification_9-36.mp44 N" U, D: m/ T
│ │ 04_Kernel_Logistic_Regression_16-22.mp4
: {, c+ M) t6 z) y │ │ ' t8 a2 b* C- T4 I
│ ├─06_Support_Vector_Regression
9 r0 S7 X3 g7 k! r4 `! z │ │ 01_Kernel_Ridge_Regression_17-17.mp4
" z' T0 y j# |6 M' ^) m; j6 t4 E │ │ 01_Kernel_Ridge_Regression_17-17.pdf
" Z6 y- h3 t, V* [; n │ │ 02_Support_Vector_Regression_Primal_18-44.mp4
3 R( N7 T( r4 t4 Q( S4 \ │ │ 03_Support_Vector_Regression_Dual_13-05.mp4
' H+ \% S6 X" X, ]& V- K+ l; B/ k │ │ 04_Summary_of_Kernel_Models_09-06.mp4
+ f4 F6 ^5 k3 o' R. ?8 R) Q │ │
; T: a [5 F/ n6 h │ ├─07_Blending_and_Bagging
. }, M- T" ^' H s4 P; G7 E! U6 ~8 N& O │ │ 01_Motivation_of_Aggregation_18-54.mp48 p6 \# A- a$ p3 Q" V: @
│ │ 01_Motivation_of_Aggregation_18-54.pdf& ?# Z5 q" j2 R6 M% l
│ │ 02_Uniform_Blending_20-31.mp4
% Q( K" }# K, O& }; E │ │ 03_Linear_and_Any_Blending_16-48.mp4' r- P8 H" H! x+ _
│ │ 04_Bagging_Bootstrap_Aggregation_11-48.mp4
& D8 k* W- T' Y) S8 l │ │
* m0 {" Q$ i! J4 I" U3 Z3 O( S7 q │ ├─08_Adaptive_Boosting! P7 |$ ^2 J5 E2 ~8 s1 `8 |
│ │ 01_Motivation_of_Boosting_12-47.mp4
% d8 ^3 o$ u! M+ F9 G* ] │ │ 01_Motivation_of_Boosting_12-47.pdf
% y U1 M C4 O& Y) Z │ │ 02_Diversity_by_Re-weighting_14-28.mp4
6 u. r3 L) W& `3 b% r# l! l5 [$ T │ │ 03_Adaptive_Boosting_Algorithm_13-34.mp40 Y" c9 b/ S4 A6 [6 o0 H9 q1 C8 U
│ │ 04_Adaptive_Boosting_in_Action_11-04.mp4
* F9 b* ^; ?, v8 x% L │ │ 4 ]7 J- z" v n2 N- q3 k) Y# L
│ ├─09_Decision_Tree7 K- x. j8 E# B' K! L6 j1 E
│ │ 01_Decision_Tree_Hypothesis_17-28.mp44 f& ]4 w$ k6 [- _/ S" i& W* M
│ │ 01_Decision_Tree_Hypothesis_17-28.pdf
; g9 }9 ^3 P. @+ s" \1 O │ │ 02_Decision_Tree_Algorithm_15-20.mp4
7 s& Q D; }+ ]7 T │ │ 03_Decision_Tree_Heuristics_in_CRT_13-21.mp4, k* W: U/ e9 o& f; k
│ │ 04_Decision_Tree_in_Action_8-44.mp4
3 l! B) s$ @4 G& q4 c8 a7 j │ │
+ S4 p2 T8 r' j ] │ ├─10_Random_Forest$ N1 b, I S1 y5 R4 Q
│ │ 01_Random_Forest_Algorithm_13-06.mp40 G, v4 f* l3 C4 [4 w4 }3 }: @
│ │ 01_Random_Forest_Algorithm_13-06.pdf0 C( }5 G' t! W, o8 _
│ │ 02_Out-Of-Bag_Estimate_12-31.mp41 y& y! R) u# @4 O5 @+ v
│ │ 03_Feature_Selection_19-27.mp4: d! i* }9 a$ A V3 t! W) L1 @1 A
│ │ 04_Random_Forest_in_Action13-28.mp4
+ U! k: R6 P3 b0 T7 b% Y9 { │ │
3 T# _7 P( i; f4 M2 R │ ├─11_Gradient_Boosted_Decision_Tree
; p/ v7 l6 |( d8 u │ │ 01_Adaptive_Boosted_Decision_Tree_15-05.mp4
" Z6 A# m7 x1 c │ │ 01_Adaptive_Boosted_Decision_Tree_15-05.pdf
. i! Y; y6 {4 F0 ^( h5 D8 B │ │ 02_Optimization_View_of_AdaBoost_27-25.mp4$ e8 |- w: J/ `) K' D
│ │ 03_Gradient_Boosting_18-20.mp4' R4 s) [' `2 X w' k, s+ m" V7 Q6 D
│ │ 04_Summary_of_Aggregation_Models_11-19.mp4% |) k, V3 g; s t. E- b# z
│ │
8 J) k8 M3 R' K9 G( K │ ├─12_Neural_Network4 `( O: P6 t! k' @
│ │ 01_Motivation_20-36.mp4
- p, O4 m$ G; m2 n │ │ 01_Motivation_20-36.pdf& ^! J7 X: u) c, I0 M* ^
│ │ 02_Neural_Network_Hypothesis_18-01.mp4
4 v' C8 V0 K) _. v5 z │ │ 03_Neural_Network_Learning_22-26.mp4, t) ^! T1 X/ V/ V; [
│ │ 04_Optimization_and_Regularization_17-29.mp45 f! s; S& j3 u. Z! V& J% o% {
│ │ 7 q% O7 m, l0 f @, K9 r% f5 @, G
│ ├─13_Deep_Learning
3 n7 y# e& {, {6 S, o8 h: A& A │ │ 01_Deep_Neural_Network_21-30.mp42 P4 d, C% X) B. c( I
│ │ 01_Deep_Neural_Network_21-30.pdf& X( k, k7 X' ?) P3 R
│ │ 02_Autoencoder_15-17.mp4) Q" y1 G0 `; C6 s% q3 b0 T c
│ │ 03_Denoising_Autoencoder_8-30.mp49 x, R- ^9 ~: R( `, b q
│ │ 04_Principal_Component_Analysis_31-20.mp41 n$ _0 w* w$ V8 [* H, `
│ │ & m4 ^* ^" G* r+ V3 b
│ ├─14_Radial_Basis_Function_Network
- ]) _) t6 \; |5 [% _2 v: j% h │ │ 01_RBF_Network_Hypothesis_12-55.mp4
- `" x8 D' F% `6 Q6 Z; ^ │ │ 01_RBF_Network_Hypothesis_12-55.pdf
6 N" c8 N# b; }% S │ │ 02_RBF_Network_Learning_20-08.mp4; F+ _. a" ^3 N/ ^% c+ s6 {
│ │ 03_k-Means_Algorithm_16-19.mp46 K, @2 b! P6 L" c4 o" h( i
│ │ 04_k-Means_and_RBF_Network_in_Action_9-46.mp4
' X1 s: H2 z' Z+ o0 R% }$ B │ │
1 V/ {( |3 `9 [ │ ├─15_Matrix_Factorization
3 B! u* P1 ]: {* y @8 c9 m/ j3 k │ │ 01_Linear_Network_Hypothesis_20-16.mp4
# X) }* \6 L& Z+ E/ _$ k$ ?0 i │ │ 01_Linear_Network_Hypothesis_20-16.pdf3 n4 f3 r8 F7 Q% V# B
│ │ 02_Basic_Matrix_Factorization_16-32.mp4/ b0 t! n1 O( }' l" F
│ │ 03_Stochastic_Gradient_Descent_12-22.mp4. F A. |; ]! n, {$ ~8 P/ G. S$ S+ f
│ │ 04_Summary_of_Extraction_Models_9-12.mp43 ~9 y0 f, f! b6 }0 }) ^, W
│ │ - h. e+ Q& ]3 E4 z' Y
│ └─16_Finale y& s1 r" }. e Z! E# J
│ 01_Feature_Exploitation_Techniques_16-11.mp4
% L$ @: R8 m1 b# Y! Y7 f │ 01_Feature_Exploitation_Techniques_16-11.pdf! }4 @( ~1 y; i2 q8 L. ~) H
│ 02_Error_Optimization_Techniques_8-40.mp4
6 l5 k8 a" n/ W0 ^- z │ 03_Overfitting_Elimination_Techniques_6-44.mp4) I# _ G/ @! Y6 G. }$ u3 P, z7 {
│ 04_Machine_Learning_in_Action_12-59.mp4
5 }: E$ U. A; a& y │
3 d4 ]: Q+ J/ H \) W( B; h ├─005_Neural Networks for Machine Learning
3 O8 @9 l7 _' W( n. }9 ]6 V │ └─005_Neural Networks for Machine Learning
9 j' R; j6 r7 k o h! D2 c3 f │ │ 下载说明.txt4 I0 G1 S0 E/ e! v& G' i; \
│ │ 关注我们.png
% c# C4 P# p$ d3 [' n$ H! ?$ @ │ │ 攻城狮论坛=网络技术+编程视频.url- \6 N# i: a+ S" c* t# E& i
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
, N0 P3 t f! w* _ │ │
$ X) t- l P5 E │ ├─hinton-ml
& I3 s t( T* i1 A7 e │ │ 1.Why do we need machine learning
& t/ ]+ \* p$ p' s │ │ 1.Why do we need machine learning.mp4
2 U) j% ~4 e: g7 m; { A$ Z: D │ │ 10.What perceptrons can't do [15 min].mp4* F! O" R3 s9 C/ y* Z, M
│ │ 10.What perceptrons can't do [15 min].srt# ?4 D0 O. ]/ f7 E! [1 k
│ │ 11.Learning the weights of a linear neuron [12 min].mp4+ p$ j( y5 }. h9 V4 @' b
│ │ 11.Learning the weights of a linear neuron [12 min].srt. b+ Q p+ @4 [1 a3 I- P9 s
│ │ 12.The error surface for a linear neuron [5 min].mp4
+ ?; L7 { N: k1 G, ` │ │ 12.The error surface for a linear neuron [5 min].srt
1 K' C3 ]' e6 J7 @3 Q* u; z4 j │ │ 13.Learning the weights of a logistic output neuron [4 min].mp4+ }7 e3 A/ G# u& p9 _0 \* E! V
│ │ 13.Learning the weights of a logistic output neuron [4 min].srt. D2 {$ f: n1 s: J5 A
│ │ 14.The backpropagation algorithm [12 min].mp4
* O( X3 I, T. e. s' ]+ [8 {5 j │ │ 14.The backpropagation algorithm [12 min].srt
: [' A# d% V, z* n2 e9 P │ │ 15.Using the derivatives computed by backpropagation [10 min].mp4
1 H0 M7 l: o' n │ │ 15.Using the derivatives computed by backpropagation [10 min].srt
" f/ p# g& C7 B* S" x │ │ 16.Learning to predict the next word [13 min].mp49 s* h% T) ^, r
│ │ 16.Learning to predict the next word [13 min].srt
6 e9 f, v' W; @& i' L7 n$ w │ │ 17.A brief diversion into cognitive science [4 min].mp4
5 x+ w2 [# O1 i( \/ s │ │ 17.A brief diversion into cognitive science [4 min].srt$ o% M) |& T0 R' }
│ │ 19.Neuro-probabilistic language models [8 min].mp4
+ t7 w9 l. A6 I0 K- r2 g │ │ 19.Neuro-probabilistic language models [8 min].srt
0 J4 i5 v9 }0 {; R │ │ 2.What are neural networks
& S+ x/ I0 X) g. G7 Q! |* |3 l; x0 F │ │ 2.What are neural networks.mp4
, @4 w9 Y1 U/ G0 F& } D8 z& ^/ j │ │ 20.Ways to deal with the large number of possible outputs [15 min].mp4
0 C& }' d, }0 {7 d; X" U4 A │ │ 20.Ways to deal with the large number of possible outputs [15 min].srt* x2 ?, q/ L% X
│ │ 21.Why object recognition is difficult [5 min].mp4
! i5 N, W" \0 ~" y │ │ 21.Why object recognition is difficult [5 min].srt
. f. V, [9 q3 m' a: r- P3 v; D- Y9 y! j │ │ 22.Achieving viewpoint invariance [6 min].mp41 F% q+ T8 S, w' }/ I
│ │ 22.Achieving viewpoint invariance [6 min].srt
; m4 l5 Z- R* d2 i' g │ │ 23.Convolutional nets for digit recognition [16 min].mp4( d# f! l8 m" A7 S* p3 W
│ │ 23.Convolutional nets for digit recognition [16 min].srt
4 N: Z, N% ]; t) i │ │ 24.Convolutional nets for object recognition [17min].mp4
$ G* p( y" J( Z% K7 l │ │ 24.Convolutional nets for object recognition [17min].srt# }& B" g P! g8 S( H
│ │ 25.Overview of mini-batch gradient descent.mp4
3 M! k2 m! R6 `: G │ │ 25.Overview of mini-batch gradient descent.srt2 @8 \8 A2 S& \. v
│ │ 26.A bag of tricks for mini-batch gradient descent.mp4* Z H! V; m Z* q
│ │ 26.A bag of tricks for mini-batch gradient descent.srt( Z! o0 z3 P# ~8 Z' b2 A* f
│ │ 27.The momentum method.mp4! E! l$ U9 e* u8 p2 c
│ │ 27.The momentum method.srt
/ g" x# i6 }2 z' x; u │ │ 28.Adaptive learning rates for each connection.mp4
m5 f! }8 x/ x, a │ │ 28.Adaptive learning rates for each connection.srt9 o. K' G0 Q1 w7 [9 c
│ │ 3.Some simple models of neurons [8 min].mp44 U$ O$ F# X- w) b3 E9 _; R# F
│ │ 3.Some simple models of neurons [8 min].srt
: ]1 \' v7 o* j6 L │ │ 31.Training RNNs with back propagation.mp4
4 h+ L! h O3 {$ q% p │ │ 31.Training RNNs with back propagation.srt
$ M7 Q6 Z: }3 P# Y( B$ t │ │ 32.A toy example of training an RNN.mp4: X( e& J# n+ D- {) z; H4 X
│ │ 32.A toy example of training an RNN.srt
: w7 S. k$ ?1 e5 |5 \) R+ Z │ │ 33.Why it is difficult to train an RNN.mp4, v0 P6 o4 K: Z! I5 G9 q0 [$ z( v3 e0 X
│ │ 33.Why it is difficult to train an RNN.srt1 N9 `" f) k2 w* s: O; k/ o
│ │ 34.Long-term Short-term-memory.mp44 }# A- x/ C: t$ n
│ │ 34.Long-term Short-term-memory.srt
4 p0 `3 m4 h. x8 y% I │ │ 35.A brief overview of Hessian Free optimization.mp4
. K8 s! Z* t' \8 V7 s │ │ 35.A brief overview of Hessian Free optimization.srt
: m4 g. z/ V( ~ │ │ 37.Learning to predict the next character using HF [12 mins].mp48 K2 d6 j3 h2 @2 h; s( m
│ │ 37.Learning to predict the next character using HF [12 mins].srt) z! x) @# V! E& ^
│ │ 38.Echo State Networks [9 min].mp4' c1 N; M0 g( o6 T, _6 L
│ │ 38.Echo State Networks [9 min].srt
: N$ u x7 K' l" i3 a4 A │ │ 39.Overview of ways to improve generalization [12 min].mp4# f0 E& g. o2 d# R* |8 t* t* I
│ │ 39.Overview of ways to improve generalization [12 min].srt* u$ s1 o& r8 f/ ^3 Y2 R
│ │ 4.A simple example of learning [6 min].mp4
% B: e l1 I2 g' O. m$ h; ^ │ │ 4.A simple example of learning [6 min].srt
! U2 h0 P+ O/ Y7 p │ │ 40.Limiting the size of the weights [6 min].mp4$ l4 [2 |7 V- D. p2 ]
│ │ 40.Limiting the size of the weights [6 min].srt
: L8 E0 d$ E Q) F U4 _ │ │ 41.Using noise as a regularizer [7 min].mp4+ G; H0 g& ^2 U9 {" w# K9 q
│ │ 41.Using noise as a regularizer [7 min].srt0 ]8 \9 ?, l; ?
│ │ 42.Introduction to the full Bayesian approach [12 min].mp4
# t& e; _6 t1 |8 S │ │ 42.Introduction to the full Bayesian approach [12 min].srt
7 T) O+ w- n4 f8 p: U │ │ 43.The Bayesian interpretation of weight decay [11 min].mp4
+ M7 L1 P, T; A ?& z │ │ 43.The Bayesian interpretation of weight decay [11 min].srt, U+ h7 _# Q) K1 E! Z ~
│ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].mp4
" S9 K- F. p0 T1 _& o │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].srt
+ H1 P& `0 v# _+ ` │ │ 45.Why it helps to combine models [13 min].mp4
7 m) |4 O! A8 |1 O y, Q2 c │ │ 45.Why it helps to combine models [13 min].srt
! j% ^1 v1 L2 Z$ l │ │ 46.Mixtures of Experts [13 min].mp4
# i9 n f. u& J5 a' `2 H$ x3 l │ │ 46.Mixtures of Experts [13 min].srt3 Y2 G! _7 \1 n2 M% W, ?
│ │ 47.The idea of full Bayesian learning [7 min].mp4
5 R5 t1 g0 B- |0 q │ │ 47.The idea of full Bayesian learning [7 min].srt# {1 f7 K u# j
│ │ 48.Making full Bayesian learning practical [7 min].mp4
6 }' t1 ~% M. ]) f │ │ 48.Making full Bayesian learning practical [7 min].srt; v6 X9 Q$ f% k
│ │ 49.Dropout [9 min].mp47 t1 \8 E3 `6 ?& ~5 v/ I0 B
│ │ 49.Dropout [9 min].srt) R. W- q- Z& Q5 v0 t
│ │ 5.Three types of learning [8 min].mp4
# I! f, |! X' m5 M5 X& b2 a n │ │ 5.Three types of learning [8 min].srt
& s0 F0 a9 H) q0 L4 [3 d │ │ 50.Hopfield Nets [13 min].mp48 B$ m* m! x1 _* p! {: Z
│ │ 50.Hopfield Nets [13 min].srt3 L9 h3 E9 Y. O( u% A3 r- B, W$ U
│ │ 51.Dealing with spurious minima [11 min].mp4. {8 I2 C# u* c6 j% U
│ │ 51.Dealing with spurious minima [11 min].srt) X6 J- ]% e* v7 V' f, y
│ │ 52.Hopfield nets with hidden units [10 min].mp42 |! r+ T3 Q7 f8 |
│ │ 52.Hopfield nets with hidden units [10 min].srt6 U6 y1 L, E& ?, b) S
│ │ 53.Using stochastic units to improv search [11 min].mp4
3 i3 A2 f; y# W9 Y9 E │ │ 53.Using stochastic units to improv search [11 min].srt
2 y5 i3 v% S: p( |% z% [( A │ │ 54.How a Boltzmann machine models data [12 min].mp4& }6 O# t5 Q$ }6 h: G
│ │ 54.How a Boltzmann machine models data [12 min].srt0 }! w" T3 e( E2 {' ]/ ~( }
│ │ 55.Boltzmann machine learning [12 min].mp4
( d5 g9 \ c7 I7 I │ │ 55.Boltzmann machine learning [12 min].srt8 O1 i! d" X* A; R* J) w* a
│ │ 57.Restricted Boltzmann Machines [11 min].mp4
' S; o; O; N" A │ │ 57.Restricted Boltzmann Machines [11 min].srt3 f( }* A( \/ H3 q
│ │ 58.An example of RBM learning [7 mins].mp4 R1 M9 K6 ?- q, C8 n2 Y
│ │ 58.An example of RBM learning [7 mins].srt5 p3 ^0 S' {( |- W- R. x) h5 M
│ │ 59.RBMs for collaborative filtering [8 mins].mp4" Y- v5 H! t8 `- e6 U' k9 g- l9 x
│ │ 59.RBMs for collaborative filtering [8 mins].srt
: M$ J v6 ^+ j+ [ │ │ 6.Types of neural network architectures [7 min].mp42 I# U* G) x) Q
│ │ 6.Types of neural network architectures [7 min].srt: g8 b- D L+ `+ g+ I) h1 `; o. X' }
│ │ 60.The ups and downs of back propagation [10 min].mp4
/ ]- y, S1 k ~+ J. n │ │ 60.The ups and downs of back propagation [10 min].srt
4 P4 b. I( `& y$ h) O, q │ │ 61.Belief Nets [13 min].mp4
) ]0 C/ ?5 s5 ~" v) n( M0 l# I │ │ 61.Belief Nets [13 min].srt5 u+ L" m! S$ H8 _' N
│ │ 62.Learning sigmoid belief nets [12 min].mp4
{. N1 |* ]& @+ z8 F- f# W$ h* V │ │ 62.Learning sigmoid belief nets [12 min].srt; W0 J" `# p" d3 ?0 R, j
│ │ 63.The wake-sleep algorithm [13 min].mp4
: H8 G! \' m, I1 ^" x │ │ 63.The wake-sleep algorithm [13 min].srt
" m- t7 ?3 y- K; [ │ │ 64.Learning layers of features by stacking RBMs [17 min].mp4
+ I" S$ h: _5 Z. G0 A │ │ 64.Learning layers of features by stacking RBMs [17 min].srt
& b. C2 g3 b, {: ^ │ │ 65.Discriminative learning for DBNs [9 mins].mp49 @6 z! i; {& M0 o8 y6 W
│ │ 65.Discriminative learning for DBNs [9 mins].srt
* R+ P! e3 o( L) r8 @% P( P │ │ 66(1).What happens during discriminative fine-tuning0 b9 r* k% j- |0 I' w
│ │ 66.What happens during discriminative fine-tuning( X' Q7 | Z6 Q1 P/ H
│ │ 67.Modeling real-valued data with an RBM [10 mins].mp45 v7 E; x* ?+ M6 S0 n
│ │ 67.Modeling real-valued data with an RBM [10 mins].srt
+ H, _' Z7 w( z) r │ │ 69.From PCA to autoencoders [5 mins].mp4
- s, ^$ ?) f# R y0 P% y │ │ 69.From PCA to autoencoders [5 mins].srt# |9 }3 e. i% C; _+ a* C* s# u1 L
│ │ 70.Deep auto encoders [4 mins].mp4. G* P2 R8 O p! `" l3 f* j
│ │ 70.Deep auto encoders [4 mins].srt
7 a7 ^' \0 \9 e9 x) x0 m* D3 a │ │ 71.Deep auto encoders for document retrieval [8 mins].mp42 t. X6 C+ _% Y$ s$ T
│ │ 71.Deep auto encoders for document retrieval [8 mins].srt
8 e* R3 i& ~. z- n7 J! w$ I │ │ 72.Semantic Hashing [9 mins].mp4$ s8 y" T3 Y; ^& b
│ │ 72.Semantic Hashing [9 mins].srt1 [3 ~+ c; [. ?/ ?' S5 X
│ │ 73.Learning binary codes for image retrieval [9 mins].mp4
, b0 D R* z1 T! R │ │ 73.Learning binary codes for image retrieval [9 mins].srt
: f# D; ^% X3 X2 ?0 S$ k: u) p │ │ 74.Shallow autoencoders for pre-training [7 mins].mp4
: z7 B( Y% e# h3 ? │ │ 74.Shallow autoencoders for pre-training [7 mins].srt8 e# {/ f4 O! H' b1 d, }: \
│ │ 8.A geometrical view of perceptrons [6 min].mp4$ I- J) }/ B, u2 M5 w
│ │ 8.A geometrical view of perceptrons [6 min].srt
' J' P9 v7 K. p# Q1 L% @ │ │ 9.Why the learning works [5 min].mp4
* i# Z: U& |' L6 u │ │ 9.Why the learning works [5 min].srt& d' X) {! E) x
│ │ # I" J4 d& z7 F8 F A
│ └─neuralnets-2012-001" b# D8 s5 A1 I- B
│ ├─01_Lecture15 B4 C3 T& t' W! G/ c1 X0 ^
│ │ 01_Why_do_we_need_machine_learning_13_min.mp4
f g& M! h$ l7 U. m. C; ~( K5 u │ │ 01_Why_do_we_need_machine_learning_13_min.pdf
; i: T1 B. ^! W# T7 d$ i3 S# ?1 ` │ │ 01_Why_do_we_need_machine_learning_13_min.pptx+ s" j K( X t+ a# y4 p# e3 [
│ │ 01_Why_do_we_need_machine_learning_13_min.srt
- |3 O) e7 q) y9 o3 g- ?6 D │ │ 01_Why_do_we_need_machine_learning_13_min.txt2 p3 \. B/ t, G
│ │ 02_What_are_neural_networks_8_min.mp4/ D: M! G- u1 `% r; n1 Q
│ │ 02_What_are_neural_networks_8_min.srt( O S& K b, ]% H% Z7 E
│ │ 02_What_are_neural_networks_8_min.txt Q9 k4 @9 s u0 b8 Q G
│ │ 03_Some_simple_models_of_neurons_8_min.mp40 L8 y1 t& T8 }" T. ]2 M1 P
│ │ 03_Some_simple_models_of_neurons_8_min.srt! x& r7 Z6 i8 A L
│ │ 03_Some_simple_models_of_neurons_8_min.txt
: b. j; ]! N7 r8 {9 S │ │ 04_A_simple_example_of_learning_6_min.mp4, i: b5 F; R4 o7 A. _' g" C
│ │ 04_A_simple_example_of_learning_6_min.srt7 O+ T0 j8 d/ p% d8 m k b
│ │ 04_A_simple_example_of_learning_6_min.txt) C( {0 q& x5 I5 e
│ │ 05_Three_types_of_learning_8_min.mp48 N9 W! H7 U: t9 e* R% L+ W
│ │ 05_Three_types_of_learning_8_min.srt8 N) I0 u1 G% V. v' g* g4 D
│ │ 05_Three_types_of_learning_8_min.txt0 w, t ~' G5 Y" Q
│ │
. X# p" b# `+ v. I/ |. ~6 H │ ├─02_Lecture2
" L+ T/ q1 K0 q* A- M7 e │ │ 03_A_geometrical_view_of_perceptrons_6_min.mp4+ Q# }/ I* S% W8 \9 Q
│ │ 03_A_geometrical_view_of_perceptrons_6_min.srt
" ?9 ~1 i# {- y+ S) ?) y: J │ │ 03_A_geometrical_view_of_perceptrons_6_min.txt
6 L: _- H! x( ]+ V │ │ 04_Why_the_learning_works_5_min.mp4/ N: v1 W# `/ q; g
│ │ 04_Why_the_learning_works_5_min.srt
4 l: C( f% u6 } N& v │ │ 04_Why_the_learning_works_5_min.txt
7 c3 W0 f0 G9 p& E* i s% d │ │ 05_What_perceptrons_cant_do_15_min.mp4
: ]+ m0 A. @ z, h2 h │ │ 05_What_perceptrons_cant_do_15_min.srt
5 D W4 @7 N" A7 Y! f6 A7 Z │ │ 05_What_perceptrons_cant_do_15_min.txt
9 g5 w+ \0 |) j# Q. x │ │
0 c% f w) p* h# _9 k │ ├─03_Lecture3
@4 @1 S8 H- |; G7 @4 ~2 X │ │ 04_The_backpropagation_algorithm_12_min.mp4% |5 q9 ~, {9 n
│ │ 04_The_backpropagation_algorithm_12_min.pdf
4 x: ^% k0 C1 n; |; ] │ │ 04_The_backpropagation_algorithm_12_min.srt
* [5 O3 p/ G5 F0 }& \9 y. D* q& ]# H │ │ 04_The_backpropagation_algorithm_12_min.txt
% P) u7 R+ d5 P1 G0 I# t │ │ 0 u1 D9 f# E0 r- \4 A7 M% D+ I
│ ├─04_Lecture4
. ]0 x- r9 k" `; h │ │ 01_Learning_to_predict_the_next_word_13_min.mp4+ |1 H3 V/ X6 `2 f" T0 L# m; P
│ │ 01_Learning_to_predict_the_next_word_13_min.pdf! _" @- e6 _# N8 J; f# H9 X4 ]( D# f
│ │ 01_Learning_to_predict_the_next_word_13_min.pptx
- |: d! M0 k! x3 z+ i' J │ │ 01_Learning_to_predict_the_next_word_13_min.srt
; M0 w! z R j" d o# X$ d │ │ 01_Learning_to_predict_the_next_word_13_min.txt
) T+ i- u( h! H │ │ 04_Neuro-probabilistic_language_models_8_min.mp49 K6 \8 I: T( U" ?2 j) z1 ^% o3 @
│ │ 04_Neuro-probabilistic_language_models_8_min.pdf( T. k) G3 b4 I% U1 g+ i9 O
│ │ 04_Neuro-probabilistic_language_models_8_min.srt
1 Y( H/ i2 C% u1 S5 \4 P │ │ 04_Neuro-probabilistic_language_models_8_min.txt& O( C& e' V7 R4 q. Z8 ?* `2 @+ E
│ │
+ x# M7 I! [8 `# C │ ├─05_Lecture52 ?, d9 a) }; j6 w. A4 P
│ │ 01_Why_object_recognition_is_difficult_5_min.mp4) n0 Q; S" ~4 n, G
│ │ 01_Why_object_recognition_is_difficult_5_min.pdf
0 |, f$ J. ?$ s' C0 r6 j │ │ 01_Why_object_recognition_is_difficult_5_min.srt
( m k0 T6 z( s1 k' u' @4 x- x+ A │ │ 01_Why_object_recognition_is_difficult_5_min.txt6 J. S1 @' L! s( r
│ │ 02_Achieving_viewpoint_invariance_6_min.mp4- N) h2 T' B( z! }
│ │ 02_Achieving_viewpoint_invariance_6_min.srt
" s7 l. Q: n% W+ b1 Y+ F; ` │ │ 02_Achieving_viewpoint_invariance_6_min.txt
1 h2 O% I g9 q6 H0 y │ │ 2 O$ v3 m3 j' D/ L3 j8 k
│ ├─06_Lecture6+ P" F+ l/ Z9 z, n. E0 a1 Y
│ │ 01_Overview_of_mini-batch_gradient_descent.mp4
& G; K8 B- |0 L: f6 L0 W │ │ 01_Overview_of_mini-batch_gradient_descent.pdf& O( n) }3 d& A! |* [
│ │ 01_Overview_of_mini-batch_gradient_descent.pptx0 D0 J' T# P3 f" ]
│ │ 01_Overview_of_mini-batch_gradient_descent.srt% x9 D$ E- O1 R2 \4 H* E
│ │ 01_Overview_of_mini-batch_gradient_descent.txt) \9 H; X, z$ k9 T: R5 e5 ?( F3 p
│ │ 03_The_momentum_method.mp47 a) m( U, T3 q4 Q, i; K: p) t
│ │ 03_The_momentum_method.srt+ c; p$ p I! q; u* Q: P7 w
│ │ 03_The_momentum_method.txt
/ f# _* { g& s2 G# W$ f │ │
# n6 B4 [: b( u: T │ ├─07_Lecture7
7 K% |# |% U8 X4 R1 {7 T+ I P │ │ 01_Modeling_sequences-_A_brief_overview.mp4
Y+ F8 N5 `' z- z │ │ 01_Modeling_sequences-_A_brief_overview.pdf+ H% D! S6 c. S! _: H+ d* Y
│ │ 01_Modeling_sequences-_A_brief_overview.pptx
, V0 l9 A7 C$ P9 l* k5 o1 M8 p │ │ 01_Modeling_sequences-_A_brief_overview.srt
4 G) X* M+ j" Q │ │ 01_Modeling_sequences-_A_brief_overview.txt
6 ~* J, a& z" T; D8 R1 X- x" B │ │ 02_Training_RNNs_with_back_propagation.mp4
' E* L6 O3 E) w9 Q │ │ 02_Training_RNNs_with_back_propagation.srt
% ^( r1 H0 f+ `/ { │ │ 02_Training_RNNs_with_back_propagation.txt; S8 Y, t3 h) i# i& J1 n, A
│ │ 03_A_toy_example_of_training_an_RNN.mp4
8 u1 @' g0 c* I8 P" t7 t1 S: k │ │ 03_A_toy_example_of_training_an_RNN.srt
# b6 a# c! i3 M │ │ 03_A_toy_example_of_training_an_RNN.txt
6 O) z2 y5 I5 G' N+ s6 y │ │ 04_Why_it_is_difficult_to_train_an_RNN.mp45 h4 P/ V5 u& v5 a4 e2 j- O! h) N
│ │ 04_Why_it_is_difficult_to_train_an_RNN.srt
9 B1 ~! x4 ]; B9 R& s │ │ 04_Why_it_is_difficult_to_train_an_RNN.txt5 _' i$ {0 z1 T
│ │ 05_Long-term_Short-term-memory.mp4
! m8 x6 o5 X+ q │ │ 05_Long-term_Short-term-memory.pdf
U9 Y5 b% L- y. X8 f6 `% M. j' s │ │ 05_Long-term_Short-term-memory.srt$ d/ s& W7 h5 q; `
│ │ 05_Long-term_Short-term-memory.txt Y _$ q: f9 F
│ │ & L; q* M6 r+ E" ? K% x9 B
│ ├─08_Lecture8. l$ U- R3 o, p. W$ T
│ │ 04_Echo_State_Networks_9_min.mp4" Q {6 b3 @9 g. c6 W0 R8 p4 _
│ │ 04_Echo_State_Networks_9_min.srt
( g0 \5 i+ M- B# J" v. S │ │ 04_Echo_State_Networks_9_min.txt- Z' i3 w. E& }8 ?. L
│ │
. L, ?- q. k' k4 z4 P* d. u7 l- M' P │ ├─09_Lecture92 Z% n, K/ E3 j2 A# }9 K; m
│ │ 02_Limiting_the_size_of_the_weights_6_min.mp4
7 b+ c" f& g, V# r │ │ 02_Limiting_the_size_of_the_weights_6_min.srt5 [$ ]) H2 I1 v' f5 l
│ │ 02_Limiting_the_size_of_the_weights_6_min.txt
5 W1 x9 I! v2 W+ f) v% N2 {; ? │ │ 03_Using_noise_as_a_regularizer_7_min.mp4
: r6 S+ U: H: S5 Y/ r. U* ^ │ │ 03_Using_noise_as_a_regularizer_7_min.srt- V7 [# S) w8 b
│ │ 03_Using_noise_as_a_regularizer_7_min.txt
. Q) l4 f& W' U6 u! c) o: O" Z │ │ " f0 ]5 ]* t: I. I0 N, \
│ ├─10_Lecture10& d6 S: h) n( T
│ │ 01_Why_it_helps_to_combine_models_13_min.mp4( }; ?- O* `; W- k$ \
│ │ 01_Why_it_helps_to_combine_models_13_min.pdf6 u1 j0 T( _" M2 s" i% I" @
│ │ 01_Why_it_helps_to_combine_models_13_min.pptx
2 {1 l. g+ d2 \7 R │ │ 01_Why_it_helps_to_combine_models_13_min.srt
/ f# y. R9 h7 E' O1 |/ g A+ M# [; q │ │ 01_Why_it_helps_to_combine_models_13_min.txt
2 y, M3 T$ l0 c I5 K │ │ 02_Mixtures_of_Experts_13_min.mp4
8 m/ Y) d3 ~2 N8 v R Q │ │ 02_Mixtures_of_Experts_13_min.pdf
, f- z, I5 g; L2 N9 W; | │ │ 02_Mixtures_of_Experts_13_min.srt- A1 x# `6 K& D% |
│ │ 02_Mixtures_of_Experts_13_min.txt0 ~3 f$ |0 l8 F1 O$ W
│ │ 03_The_idea_of_full_Bayesian_learning_7_min.mp48 s8 ~; {( L1 q+ T0 k( _
│ │ 03_The_idea_of_full_Bayesian_learning_7_min.srt
4 X% E: ?, N7 {3 k7 Z1 g │ │ 03_The_idea_of_full_Bayesian_learning_7_min.txt
2 [+ V, E1 i3 M# X6 p& N+ f5 q │ │ 05_Dropout_9_min.mp4
7 F: _- E Z$ P0 ] │ │ 05_Dropout_9_min.pdf o7 Z" h; n" C5 {/ [
│ │ 05_Dropout_9_min.srt: P$ w6 [/ Z& R+ t9 R5 J
│ │ 05_Dropout_9_min.txt2 E+ A/ D4 F t/ L
│ │ ! w4 _3 \0 t4 D+ E
│ ├─11_Lecture11
1 J! H4 l+ ` A+ ?! K D │ │ 01_Hopfield_Nets_13_min.mp4$ M+ u$ X* a2 l# O: t- w
│ │ 01_Hopfield_Nets_13_min.pdf2 h* L |# W# s# a* {8 H- E1 g
│ │ 01_Hopfield_Nets_13_min.pptx5 h: U/ W& l* ^% i3 {
│ │ 01_Hopfield_Nets_13_min.srt8 d+ b, l! W& U) T. y9 r
│ │ 01_Hopfield_Nets_13_min.txt0 |1 @' `4 ~' i& O
│ │ 02_Dealing_with_spurious_minima_11_min.mp4
4 f# t4 p0 ~. F" Z │ │ 02_Dealing_with_spurious_minima_11_min.srt$ F$ n' R# o# }* Z- G% W
│ │ 02_Dealing_with_spurious_minima_11_min.txt
1 n r; i9 z/ v5 Y' Z, h9 q$ O │ │ 03_Hopfield_nets_with_hidden_units_10_min.mp4
! c- V+ _9 k0 w! K% j; Z% z2 Z( R6 P │ │ 03_Hopfield_nets_with_hidden_units_10_min.srt- [# g/ J% t: ^- E
│ │ 03_Hopfield_nets_with_hidden_units_10_min.txt
+ |& m$ Y6 D! [' _ │ │ 0 p2 v% N6 R& K
│ ├─12_Lecture12
! j1 q: q( @6 C; G& t5 i2 `5 _; g │ │ 01_Boltzmann_machine_learning_12_min.mp4: v8 A7 O6 {* ?! Y% v9 x8 Q2 h! T
│ │ 01_Boltzmann_machine_learning_12_min.pdf' H2 K. m+ N' J+ ? Q' t' P
│ │ 01_Boltzmann_machine_learning_12_min.pptx! _# \2 Q, f2 }( R
│ │ 01_Boltzmann_machine_learning_12_min.srt8 J3 ~) Q3 g! v( O
│ │ 01_Boltzmann_machine_learning_12_min.txt
j" ?, ]" a% g: d$ Z │ │ 03_Restricted_Boltzmann_Machines_11_min.mp44 C O+ P' P# H; Y$ T
│ │ 03_Restricted_Boltzmann_Machines_11_min.srt
1 F5 ^1 @1 _0 C8 r+ q5 r3 \ │ │ 03_Restricted_Boltzmann_Machines_11_min.txt
, H2 x7 Z* }$ i j g6 o │ │ 04_An_example_of_RBM_learning_7_mins.mp42 x$ Y# t# C; n6 q! c9 Q
│ │ 04_An_example_of_RBM_learning_7_mins.srt$ n1 `7 A3 i& }
│ │ 04_An_example_of_RBM_learning_7_mins.txt; Z3 Q' U1 }, d7 o' Y
│ │ 05_RBMs_for_collaborative_filtering_8_mins.mp4- ~8 R f# S3 J7 o# z2 d# N
│ │ 05_RBMs_for_collaborative_filtering_8_mins.srt/ j3 ]# | h+ u; F; q! x
│ │ 05_RBMs_for_collaborative_filtering_8_mins.txt; y" ~6 q9 e0 E* s3 K+ V. l
│ │ 5 E6 X* h! v' {2 i1 _
│ ├─13_Lecture13
8 g7 P2 B Y& o( J9 E │ │ 02_Belief_Nets_13_min.mp40 T4 D2 g4 F5 P7 x) G0 N* S3 z
│ │ 02_Belief_Nets_13_min.srt. N j5 w6 Q+ r8 K( t
│ │ 02_Belief_Nets_13_min.txt2 A6 ~0 J2 m+ X
│ │ 03_Learning_sigmoid_belief_nets_12_min.mp48 K- ?( y( Q9 i& ^
│ │ 03_Learning_sigmoid_belief_nets_12_min.pdf
' q+ \9 X, q+ b- M4 C% W │ │ 03_Learning_sigmoid_belief_nets_12_min.srt
6 E% D, Z1 G8 b8 I4 |, m │ │ 03_Learning_sigmoid_belief_nets_12_min.txt
' G' \! u8 F: b │ │ 04_The_wake-sleep_algorithm_13_min.mp4 E# D: Z/ J( t* N; h; B- U% m {
│ │ 04_The_wake-sleep_algorithm_13_min.pdf1 v1 W: P, d7 D$ ]9 l, B
│ │ 04_The_wake-sleep_algorithm_13_min.srt9 m: [& {' Q( y. _, n2 y
│ │ 04_The_wake-sleep_algorithm_13_min.txt& l' Z: H- Z8 R; g
│ │ " e# O0 z9 n" [" O3 t& S+ G
│ ├─14_Lecture14
. d1 v1 {# n3 k4 E# Z& p" ?3 B │ │ 02_Discriminative_learning_for_DBNs_9_mins.mp4! j9 q! G( f& w( e- ~/ A
│ │ 02_Discriminative_learning_for_DBNs_9_mins.srt
0 x0 x( J( l3 t! w* K │ │ 02_Discriminative_learning_for_DBNs_9_mins.txt9 S5 U7 D/ }/ e% V2 d
│ │
/ V$ L+ ]/ f" v( F/ Q$ l- E+ h/ { │ ├─15_Lecture15
h; o4 W& f, s │ │ 01_From_PCA_to_autoencoders_5_mins.mp48 e. {3 e( V. Q; ^4 m6 x
│ │ 01_From_PCA_to_autoencoders_5_mins.pdf
5 |% {0 z1 Q# l% C1 C2 G │ │ 01_From_PCA_to_autoencoders_5_mins.pptx2 m% G5 S& Y2 V9 `& T0 ~. k
│ │ 01_From_PCA_to_autoencoders_5_mins.srt% x3 l1 F! a6 j* \& L" ^* D. h
│ │ 01_From_PCA_to_autoencoders_5_mins.txt
/ y+ h1 c% n/ k- }3 V/ _" d) k │ │ 02_Deep_auto_encoders_4_mins.mp47 y! E2 K% ^1 o; b# @+ Z
│ │ 02_Deep_auto_encoders_4_mins.srt
6 J; D: h& M0 c2 q. \ │ │ 02_Deep_auto_encoders_4_mins.txt
9 j& j$ H8 X8 { │ │ 04_Semantic_Hashing_9_mins.mp4
( ]1 l$ I t' a2 t# d# j │ │ 04_Semantic_Hashing_9_mins.pdf4 }- }0 C0 ^. r4 E
│ │ 04_Semantic_Hashing_9_mins.srt
% W. s" K( G) a+ d! T │ │ 04_Semantic_Hashing_9_mins.txt
6 }/ ?! [/ [8 J/ W$ s8 G │ │ & Y+ e: E( c c9 Y* J- M+ {
│ └─16_Lecture16
2 R c- m: n( u+ g$ b │ 04_OPTIONAL-_The_fog_of_progress_3_min.mp4+ p! T8 w2 x. ?0 L: N5 V
│ 04_OPTIONAL-_The_fog_of_progress_3_min.pdf) z5 h, c+ C j- h; k5 @$ n2 F3 K
│ 04_OPTIONAL-_The_fog_of_progress_3_min.pptx
" a: s$ D6 m' G. @ │ 04_OPTIONAL-_The_fog_of_progress_3_min.srt
; F/ v; X: X0 \& J; h │ 04_OPTIONAL-_The_fog_of_progress_3_min.txt
9 M% `& c- v- N+ h% i │
% U" n0 X" X; g3 X7 ` k ├─006_Probabilistic Graphical Models
( \3 v- X& F" w3 K │ └─006_Probabilistic Graphical Models
: `& S) ~$ V6 s/ F6 Q1 j+ m- C │ ├─pgm-003
8 ? i! Q% e: M+ X4 U7 _. g, Q │ │ ├─01_Introduction_and_Overview$ B) ?. O# p5 ]# R
│ │ │ 01_Welcome.mp4
5 ~- Y: `4 |# K. f; d! t │ │ │ 01_Welcome.srt
2 H K+ \# I$ z- z' a5 m* N │ │ │ 01_Welcome.txt" D( H( ^; R+ f& L+ v% T9 e
│ │ │ 02_Overview_and_Motivation.mp4
& y" Q1 q2 C: I: F4 o/ U │ │ │ 02_Overview_and_Motivation.srt2 n! T! k$ b3 z9 r( u/ b1 {6 ]
│ │ │ 02_Overview_and_Motivation.txt
/ ]; ~9 M; U, @1 ]/ [; W │ │ │ 03_Distributions.mp4
: a, D5 R! b: f. b9 X5 X. u │ │ │ 03_Distributions.srt0 ?' R( y8 l8 |
│ │ │ 03_Distributions.txt
: ?3 }( E$ M* ?- i │ │ │ 04_Factors.mp4# s p) h" S' v+ }1 f$ T3 l
│ │ │ 04_Factors.srt
9 R B @# V& ]' X$ E │ │ │ 04_Factors.txt) I( |" ^0 A# n9 T
│ │ │
6 D4 G8 z- G: W5 f │ │ ├─02_Bayesian_Network_Fundamentals
" ~1 D, C2 T: }6 z: x9 u │ │ │ 01_Semantics_amp_Factorization.mp4
; g C. k3 {2 H8 i$ [% w; k │ │ │ 01_Semantics_amp_Factorization.srt
$ z( {$ l6 i* g5 \+ ~' e$ j% U │ │ │ 01_Semantics_amp_Factorization.txt
* A: _# L, o1 b. t4 f. i │ │ │ 02_Reasoning_Patterns.mp4/ U* T$ z" a0 a% E
│ │ │ 02_Reasoning_Patterns.srt
, O" e, j0 w% p# H9 X │ │ │ 02_Reasoning_Patterns.txt+ }5 {, X8 V5 F9 R& J' `
│ │ │ 03_Flow_of_Probabilistic_Influence.mp4 s! Y2 I( P: i7 ~0 l
│ │ │ 03_Flow_of_Probabilistic_Influence.srt
" E* m/ h& M3 A: T% i/ @/ R │ │ │ 03_Flow_of_Probabilistic_Influence.txt* r3 k n% d1 ^7 _4 s- R1 e7 J3 S
│ │ │ 04_Conditional_Independence.mp4
' i3 A! O# G! E │ │ │ 04_Conditional_Independence.srt
$ \) b- @7 v4 b) u, f! W) n# e& v │ │ │ 04_Conditional_Independence.txt
. h& s9 ], g: m4 P │ │ │ 05_Independencies_in_Bayesian_Networks.mp45 B8 _8 l& b' B1 w: S9 X, z. F
│ │ │ 05_Independencies_in_Bayesian_Networks.srt* s1 e4 Q0 u* I6 Z# P' u; t q4 V: b
│ │ │ 05_Independencies_in_Bayesian_Networks.txt
: J" Z' I) k" ]" I: | │ │ │ 06_Naive_Bayes.mp47 c7 X8 q( ~& }& R% r3 C
│ │ │ 06_Naive_Bayes.srt* ^( N8 f& X! t7 u3 s8 T
│ │ │ 06_Naive_Bayes.txt
* e. ^7 ~+ \5 H! a5 ^& E8 Q │ │ │ 07_Application_-_Medical_Diagnosis.mp4
" U# D" l. b$ F+ T │ │ │ 07_Application_-_Medical_Diagnosis.srt
# U9 `; a8 ^9 C; d │ │ │ 07_Application_-_Medical_Diagnosis.txt- `3 e7 t8 M/ X' F# p0 ?
│ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.mp4& T9 w. _) `% M: U: ?" N5 o+ `- [
│ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.srt1 s& d! Z& [- Z; V
│ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.txt
" e: `- ~: ?! D: d7 A9 p │ │ │
4 M% g6 k! V$ ]4 J ?6 s │ │ ├─03_Template_Models
) J$ Q# `) w8 D5 Y) b │ │ │ 01_Overview_of_Template_Models.mp4
0 i$ h/ m& W! m1 t* W4 O │ │ │ 01_Overview_of_Template_Models.srt
0 e5 D' b. h: _* M# V5 ? │ │ │ 01_Overview_of_Template_Models.txt8 ~* G: U, A' e9 d( S! W
│ │ │ 02_Temporal_Models_-_DBNs.mp4
c2 [0 H( M) [2 X │ │ │ 02_Temporal_Models_-_DBNs.srt! k2 a8 B- ]/ u
│ │ │ 02_Temporal_Models_-_DBNs.txt
6 ^+ Q1 R4 M# [ c* E% b │ │ │ 03_Temporal_Models_-_HMMs.mp4
0 o$ ?4 e( B- ~; _8 |+ ` │ │ │ 03_Temporal_Models_-_HMMs.srt5 p; ?! \' Q. l. \. D% P
│ │ │ 03_Temporal_Models_-_HMMs.txt
" [0 ^ c6 E4 e9 A │ │ │ 04_Plate_Models.mp4
4 O. `6 [; @* B1 @; ]" B2 i) Q2 H │ │ │ 04_Plate_Models.srt) w" n6 t) O# b8 V! {
│ │ │ 04_Plate_Models.txt
8 o! z9 j. u/ v. W4 k2 d │ │ │ & \( g M: K) N- B$ H$ T
│ │ ├─04_ML-class_Octave_Tutorial
3 H; k V1 L: l" G │ │ │ 01_Basic_Operations.mp49 {/ y- F [0 l6 I3 ?
│ │ │ 01_Basic_Operations.srt, d5 b4 N* {+ Z2 ~2 Q8 e
│ │ │ 01_Basic_Operations.txt5 \% G9 ~5 r1 D5 B2 N, N+ `# i
│ │ │ 02_Moving_Data_Around.mp41 ]# H+ h5 j6 {" L& h7 W
│ │ │ 02_Moving_Data_Around.srt
; s/ k+ U; z) G& v5 Q9 V- d4 N │ │ │ 02_Moving_Data_Around.txt+ q! r6 ~7 D; ~
│ │ │ 03_Computing_On_Data.mp4
2 \# v" u1 |! L: F6 d │ │ │ 03_Computing_On_Data.srt
) y; p5 D' {6 D) G │ │ │ 03_Computing_On_Data.txt
U9 E J' p* L* c │ │ │ 04_Plotting_Data.mp4" C9 X/ y5 w v( M( s5 U8 y9 m* d- s+ ]
│ │ │ 04_Plotting_Data.srt
2 D: w' h# m) x. }5 H7 M& q │ │ │ 04_Plotting_Data.txt
0 H7 _0 k) [& R6 r/ p) m' a │ │ │ 05_Control_Statements-_for_while_if_statements.mp4
/ ?# i' I7 Z. Z2 N │ │ │ 05_Control_Statements-_for_while_if_statements.srt
- j2 M1 f. s4 J5 h s │ │ │ 05_Control_Statements-_for_while_if_statements.txt
9 L% X6 y; D( {" W │ │ │ 06_Vectorization.mp4+ I8 f2 f' d$ I( i3 B2 I- o
│ │ │ 06_Vectorization.srt5 ^- {& ?6 v7 \; }$ u
│ │ │ 06_Vectorization.txt. x) g7 w* H8 f) s5 D, F7 d
│ │ │ 07_Working_on_and_Submitting_Programming_Exercises.mp4& t/ a; {7 _/ A+ ]( l3 N; A: x7 Y
│ │ │ 07_Working_on_and_Submitting_Programming_Exercises.srt0 T1 m. M' r8 I
│ │ │ 07_Working_on_and_Submitting_Programming_Exercises.txt E7 c6 t( G. S- e) p# p
│ │ │ 3 q5 r' H3 `+ B2 {9 ^2 p1 i
│ │ ├─05_Structured_CPDs
8 H4 L( e- ?: i7 F( R# Z; F │ │ │ 01_Overview-_Structured_CPDs.mp4
/ [* a5 k/ @+ Y' K4 U" Y# h │ │ │ 01_Overview-_Structured_CPDs.srt
5 i3 ]* y, ~, J* o1 u │ │ │ 01_Overview-_Structured_CPDs.txt
2 |( B( B: g0 n0 z │ │ │ 02_Tree-Structured_CPDs.mp4; k' i& D# S7 f( w
│ │ │ 02_Tree-Structured_CPDs.srt+ U7 j; y* U* t q
│ │ │ 02_Tree-Structured_CPDs.txt
' p, ^) s; U$ n/ R │ │ │ 03_Independence_of_Causal_Influence.mp4( R6 k" n1 _$ B! _* q/ `9 p
│ │ │ 03_Independence_of_Causal_Influence.srt# w* T$ W" g$ D" V
│ │ │ 03_Independence_of_Causal_Influence.txt
) ~# N# A) {* a4 Y' J7 ` │ │ │ 04_Continuous_Variables.mp4
2 v. }7 y9 J" A │ │ │ 04_Continuous_Variables.srt& n# M0 T9 [/ j( d$ C* L3 w
│ │ │ 04_Continuous_Variables.txt8 d3 \1 L, ]' e) A: B
│ │ │
/ s/ y b+ y5 P │ │ ├─06_Markov_Network_Fundamentals
. _* A1 h6 h! u( p! ^* K1 \ │ │ │ 01_Pairwise_Markov_Networks.mp4% e/ K1 p7 O9 ^( \1 L9 J! V% q: f$ {! G
│ │ │ 01_Pairwise_Markov_Networks.srt1 ?% X2 m( k! ?, Z0 ]" ?- g9 z
│ │ │ 01_Pairwise_Markov_Networks.txt
$ d+ H. y6 a$ g% q │ │ │ 02_General_Gibbs_Distribution.mp49 ~- H. R/ U3 o9 V+ h: ~
│ │ │ 02_General_Gibbs_Distribution.srt) u T+ k9 g O5 `
│ │ │ 02_General_Gibbs_Distribution.txt
- u- E" B3 t( b- t. b; L! |& u& { │ │ │ 03_Conditional_Random_Fields.mp4
8 n( n9 n* F3 f# [$ i7 R8 e8 `# p │ │ │ 03_Conditional_Random_Fields.srt* Q6 g5 `2 z- c! D
│ │ │ 03_Conditional_Random_Fields.txt: r3 {* C1 g E& Q% ^; d H
│ │ │ 04_Independencies_in_Markov_Networks.mp4/ }0 c9 K1 [+ |0 @, f
│ │ │ 04_Independencies_in_Markov_Networks.srt7 V% Z, a6 z5 S/ d" {
│ │ │ 04_Independencies_in_Markov_Networks.txt
0 K' f, c6 L2 O9 e │ │ │ 05_I-maps_and_perfect_maps.mp4
W# b1 F& t6 }# S0 d: {! @ │ │ │ 05_I-maps_and_perfect_maps.srt
3 T7 E+ {' ]: U, S7 i$ [/ i │ │ │ 05_I-maps_and_perfect_maps.txt0 ^- ^8 d$ s4 F. p0 b
│ │ │ 06_Log-Linear_Models.mp4
3 F/ J4 y' }0 h+ Q+ k- M8 T │ │ │ 06_Log-Linear_Models.srt
' O, @! n8 E' m2 j$ Y │ │ │ 06_Log-Linear_Models.txt
( N8 H6 y, V! N6 s) y │ │ │ 07_Shared_Features_in_Log-Linear_Models.mp4
. T0 g1 V, I0 o2 v( n# I │ │ │ 07_Shared_Features_in_Log-Linear_Models.srt
6 }4 M" W3 B; G9 j4 J │ │ │ 07_Shared_Features_in_Log-Linear_Models.txt
- ^; q+ b6 V5 @% X; \7 t! h │ │ │
8 r4 F- c4 s. J/ Y │ │ ├─07_Representation_Wrapup-_Knowledge_Engineering) U8 \' k' Q( I% D
│ │ │ 01_Knowledge_Engineering.mp4& U' m0 @! c- Z5 Q& y' |; S
│ │ │ 01_Knowledge_Engineering.srt
7 }7 C- ^) C) M/ M) P# d. } │ │ │ 01_Knowledge_Engineering.txt- \# m8 m9 K; k. f
│ │ │ ) m& c: R( l% }
│ │ ├─08_Inference-_Variable_Elimination ~4 J0 O& d+ C: U7 o2 q" f3 ^" G
│ │ │ 01_Overview-_Conditional_Probability_Queries.mp4
% W! ]+ }* I4 ~2 z5 o* p │ │ │ 01_Overview-_Conditional_Probability_Queries.srt
^3 D9 r3 I' c7 X │ │ │ 01_Overview-_Conditional_Probability_Queries.txt
0 o6 U" w- @6 w+ r) p │ │ │ 02_Overview-_MAP_Inference.mp4
( R, T1 {* X* t5 ]/ w R │ │ │ 02_Overview-_MAP_Inference.srt5 S! t: b1 ], |. x
│ │ │ 02_Overview-_MAP_Inference.txt
& k! X9 g& ]1 n N* ]* S │ │ │ 03_Variable_Elimination_Algorithm.mp4
8 x8 V5 S( l0 R │ │ │ 03_Variable_Elimination_Algorithm.srt0 _: ~& | x% W) f( y( [1 A
│ │ │ 03_Variable_Elimination_Algorithm.txt
! c; ~0 z, L5 r B1 T, F {5 B │ │ │ 04_Complexity_of_Variable_Elimination.mp4/ z) `9 s `+ X) r2 {
│ │ │ 04_Complexity_of_Variable_Elimination.srt
$ p- Y' Y- E+ \7 { │ │ │ 04_Complexity_of_Variable_Elimination.txt' o) U9 @1 o' [' }# f% _9 q8 N
│ │ │ 06_Finding_Elimination_Orderings.mp4
/ b0 g4 J& J& p │ │ │ 06_Finding_Elimination_Orderings.srt9 P7 `8 U7 m& ~1 o2 W8 W. n( P+ _/ f
│ │ │ 06_Finding_Elimination_Orderings.txt
$ X. y F7 r( J3 F O1 E │ │ │ 1 |8 F, b8 {/ q9 L7 ]2 c" O
│ │ ├─09_Inference-_Belief_Propagation_Part_19 }8 T% E" d7 J" E P
│ │ │ 01_Belief_Propagation.mp4; t0 r- a1 H' [& o% _/ ~
│ │ │ 01_Belief_Propagation.srt
7 c. I+ J0 i! ?5 `0 o │ │ │ 01_Belief_Propagation.txt: p* z! @- ]& X9 w6 t& u
│ │ │ 02_Properties_of_Cluster_Graphs.mp4
6 \& B# N) H1 Y/ h3 Q, ~ │ │ │ 02_Properties_of_Cluster_Graphs.srt
7 @; E8 j& @5 t0 A7 {1 E/ T$ W │ │ │ 02_Properties_of_Cluster_Graphs.txt/ l8 U% Z5 Q4 f: _
│ │ │ ( i, f. x" F; }3 p) d! l
│ │ ├─10_Inference-_Belief_Propagation_Part_2& k/ j' D2 o7 x# T! G9 D6 k
│ │ │ 01_Properties_of_Belief_Propagation.mp40 B9 D" C5 Z W. X1 X I% W
│ │ │ 01_Properties_of_Belief_Propagation.srt
& W0 `5 s- Q, y) F │ │ │ 01_Properties_of_Belief_Propagation.txt
+ T* `+ v$ a4 R; } │ │ │ 02_Clique_Tree_Algorithm_-_Correctness.mp4& ~0 e$ {3 @0 ]% L" G [% l9 J: Z
│ │ │ 02_Clique_Tree_Algorithm_-_Correctness.srt
; S( m( b9 m" g }" x4 ~ │ │ │ 02_Clique_Tree_Algorithm_-_Correctness.txt
$ U9 X7 n# L8 p$ q/ C6 f │ │ │ 03_Clique_Tree_Algorithm_-_Computation.mp4
( r! b6 ^& g) X1 q. L │ │ │ 03_Clique_Tree_Algorithm_-_Computation.srt
8 _' \: v) i1 e7 ^# X& m │ │ │ 03_Clique_Tree_Algorithm_-_Computation.txt! |% J* Z- e4 i0 t! i* O
│ │ │ 04_Clique_Trees_and_Independence.mp4
) P' D8 o( ?1 Z$ y& D, x │ │ │ 04_Clique_Trees_and_Independence.srt
1 `# J( l, l E │ │ │ 04_Clique_Trees_and_Independence.txt) ~* ?+ S% q& C6 d: m! A
│ │ │ 05_Clique_Trees_and_VE.mp46 D9 }' l8 @/ @8 h& C/ r. T0 J
│ │ │ 05_Clique_Trees_and_VE.srt8 F: {1 D7 r* P5 m# r- v
│ │ │ 05_Clique_Trees_and_VE.txt
) O& I- t# a/ F( |& b │ │ │ 06_BP_In_Practice.mp4
% Q* p2 H; b, s" R/ X; K$ V1 S │ │ │ 06_BP_In_Practice.srt
1 x, T; I2 |! C; H3 J │ │ │ 06_BP_In_Practice.txt
# c+ O7 S- b7 [ │ │ │ 07_Loopy_BP_and_Message_Decoding.mp44 u" {- M1 Z: k! V
│ │ │ 07_Loopy_BP_and_Message_Decoding.srt2 E/ v$ I1 ?% d2 k+ k s/ E0 J3 I- m
│ │ │ 07_Loopy_BP_and_Message_Decoding.txt
# J' g5 V9 G+ D2 t* Q3 A, F& Z# r; z │ │ │ + b4 h. }. }0 t7 Q1 ^
│ │ ├─11_Inference-_MAP_Estimation_Part_1
1 [+ ^, Q- M0 E3 M │ │ │ 01_Max_Sum_Message_Passing.mp4" g/ A1 F' |3 L& D/ n
│ │ │ 01_Max_Sum_Message_Passing.srt: w, x2 m, _9 W j) {6 ]; x
│ │ │ 01_Max_Sum_Message_Passing.txt
9 R' j( L0 I+ y5 Y, u& o5 Y6 J+ t │ │ │ 02_Finding_a_MAP_Assignment.mp4
" s8 L" e3 t! Q& R( s │ │ │ 02_Finding_a_MAP_Assignment.srt- r! C) f- D& {$ C; e6 q# W
│ │ │ 02_Finding_a_MAP_Assignment.txt
0 F4 J) r' d/ t/ ?7 S │ │ │
; ~0 U+ Y7 ~+ i/ C │ │ ├─12_Inference-_MAP_Estimation_Part_2% N! }1 v) M0 |8 S2 g
│ │ │ 01_Tractable_MAP_Problems.mp4
6 h! J" X& K" F% e │ │ │ 01_Tractable_MAP_Problems.srt
$ @, U- C1 E1 ^3 A( m# `. g │ │ │ 01_Tractable_MAP_Problems.txt+ _ p% Z" ~, w$ B6 u- V2 F
│ │ │ 02_Dual_Decomposition_-_Intuition.mp4- }$ J$ m, U) G# }, p. u+ X
│ │ │ 02_Dual_Decomposition_-_Intuition.srt
7 f1 o5 _" A7 L' u& I! q1 `- d# Y% q │ │ │ 02_Dual_Decomposition_-_Intuition.txt& t" S. N; A- @& g3 v9 B& e
│ │ │ 03_Dual_Decomposition_-_Algorithm.mp4
( D9 d1 e5 E9 X2 a1 S2 L │ │ │ 03_Dual_Decomposition_-_Algorithm.srt
0 g' O# h# P" ?* U8 Y$ w │ │ │ 03_Dual_Decomposition_-_Algorithm.txt
# F$ ~% S( g) n; H │ │ │
$ B- C, ~ z& W+ r' ] │ │ ├─13_Inference-_Sampling_Methods/ f9 v3 S7 Y) I. S# M# {
│ │ │ 01_Simple_Sampling.mp42 ]+ w' d3 `- a% \
│ │ │ 01_Simple_Sampling.srt
, X+ G: e) A7 ^8 j, W7 L5 ?) } │ │ │ 01_Simple_Sampling.txt
. X" [' T3 P4 b. `* k+ \2 U) [ │ │ │ 02_Markov_Chain_Monte_Carlo.mp49 j* Q9 @7 G8 g0 U# e3 b
│ │ │ 02_Markov_Chain_Monte_Carlo.srt
( h E! P3 P; \9 \ │ │ │ 02_Markov_Chain_Monte_Carlo.txt3 S; {% R) J9 o8 L6 L& U6 B5 o ]
│ │ │ 03_Using_a_Markov_Chain.mp4
+ W; i) z( Q# i │ │ │ 03_Using_a_Markov_Chain.srt
. j" y/ Q) }5 Q* h: ~# p% Z' Y8 c │ │ │ 03_Using_a_Markov_Chain.txt- Z& W& g" A+ i. z
│ │ │ 04_Gibbs_Sampling.mp40 \. r, {+ A& p1 z- b
│ │ │ 04_Gibbs_Sampling.srt! h3 t0 [6 j3 j& A" O$ i
│ │ │ 04_Gibbs_Sampling.txt+ k# w; | A- e2 H
│ │ │ 05_Metropolis_Hastings_Algorithm.mp4
: E+ N [" I4 z6 P │ │ │ 05_Metropolis_Hastings_Algorithm.srt1 h6 C9 d f+ o* `( J8 @
│ │ │ 05_Metropolis_Hastings_Algorithm.txt% J8 M" T' `7 B' `* h) S" _
│ │ │
* Z" F( G Y0 I- a │ │ ├─14_Inference-_Temporal_Models_and_Wrap-up* a. c! N8 c1 F7 \/ }; K+ k' E
│ │ │ 01_Inference_in_Temporal_Models.mp4
3 E5 |1 W3 r5 v/ ~6 i │ │ │ 01_Inference_in_Temporal_Models.srt1 z5 |1 v! o+ G3 f
│ │ │ 01_Inference_in_Temporal_Models.txt
1 Z. F/ Z! \+ ]3 `! l* ] │ │ │ 02_Inference-_Summary.mp4
8 ^4 I8 W6 C. O- ?) d8 z │ │ │ 02_Inference-_Summary.srt0 R2 J1 p7 w4 u7 J) ]; d4 c) V
│ │ │ 02_Inference-_Summary.txt* R7 u$ W2 E: u, G9 V8 ^: }) m
│ │ │ 7 K G& y$ N' `% H" t2 q
│ │ ├─15_Decision_Theory: w6 |/ G9 k. u$ g# I
│ │ │ 01_Maximum_Expected_Utility.mp4
4 o7 F, ]6 O" @ │ │ │ 01_Maximum_Expected_Utility.srt1 p! I0 A" c. V" z6 W. ?
│ │ │ 01_Maximum_Expected_Utility.txt
' V) ]$ a! a; j2 N │ │ │ 02_Utility_Functions.mp4
" i+ n* N" U( K w+ h9 q- R │ │ │ 02_Utility_Functions.srt
! y& C- ?/ a! Q! l3 P │ │ │ 02_Utility_Functions.txt, o5 U7 u- w+ v# q q3 j9 _* l
│ │ │ 03_Value_of_Perfect_Information.mp4. R/ x) @0 C8 y: O& C) p$ Y6 U- [
│ │ │ 03_Value_of_Perfect_Information.srt9 y$ [* D0 Z" U+ q7 ?3 S7 ~
│ │ │ 03_Value_of_Perfect_Information.txt
7 ^2 y! Y2 m- J. h n k$ A │ │ │ & ?2 h; |. b1 F: A# \- U2 \
│ │ ├─16_ML-class_Revision
/ p* ~! g5 S. E. q9 s │ │ │ 01_Regularization-_The_Problem_of_Overfitting.mp4" R% G$ Q$ L2 p* @
│ │ │ 01_Regularization-_The_Problem_of_Overfitting.srt
' q- | C0 i7 z4 C( R( C% h │ │ │ 01_Regularization-_The_Problem_of_Overfitting.txt4 u# ^. L* W" j3 L. Q4 c2 D
│ │ │ 02_Regularization-_Cost_Function.mp44 I( w1 M) i3 T% D* `
│ │ │ 02_Regularization-_Cost_Function.srt7 O- C; a4 T! K- v% I$ G4 u
│ │ │ 02_Regularization-_Cost_Function.txt1 D$ I' `# h) R
│ │ │ 03_Evaluating_a_Hypothesis.mp4
' W- z- `$ H8 p6 S- V: u │ │ │ 03_Evaluating_a_Hypothesis.srt# M8 I1 z4 S1 g8 ]0 {6 e2 n2 s \
│ │ │ 03_Evaluating_a_Hypothesis.txt
8 R2 ~5 ^! i2 e5 S │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.mp4
' y/ c% q1 p; U3 C l* |/ E │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.srt
$ I* c6 {! }( V' U8 D- V+ g2 y3 O │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.txt
/ c V$ w+ p$ B. G8 e. y │ │ │ 05_Diagnosing_Bias_vs_Variance.mp4
5 Z$ a6 T' |4 ~6 }' _0 y │ │ │ 05_Diagnosing_Bias_vs_Variance.srt3 T; q: t- V _# h$ `" A
│ │ │ 05_Diagnosing_Bias_vs_Variance.txt
. Z' M7 l) Z9 u$ y │ │ │ 06_Regularization_and_Bias_Variance.mp41 |) S+ x8 C2 R' A( O; n
│ │ │ 06_Regularization_and_Bias_Variance.srt
: z# D' G6 D( E; ? │ │ │ 06_Regularization_and_Bias_Variance.txt, s7 c/ K1 ?- y! \
│ │ │
6 p; [/ Y% y* G% K$ I0 i1 T6 P │ │ ├─17_Learning-_Overview
5 s" d3 F! S' q: t% | b │ │ │ 01_Learning-_Overview.mp4. B: K/ Z; U/ w4 E$ l7 S' Y
│ │ │ 01_Learning-_Overview.srt$ c- k3 i4 B1 v; W( o/ i
│ │ │ 01_Learning-_Overview.txt& s* U$ H M! E) G6 K2 r1 i
│ │ │ 9 a V" D: \& K& G
│ │ ├─18_Learning-_Parameter_Estimation_in_BNs3 B9 ~' U+ {1 F7 w
│ │ │ 01_Maximum_Likelihood_Estimation.mp49 n4 B! B1 }. ]5 @* \: u7 C* ]8 J
│ │ │ 01_Maximum_Likelihood_Estimation.srt5 z" N9 U' F3 [6 Q, ~ `' w+ R4 S
│ │ │ 01_Maximum_Likelihood_Estimation.txt, b8 ~3 {; y1 G4 Z) o
│ │ │ 03_Bayesian_Estimation.mp4
& s4 f: _( _/ M& H │ │ │ 03_Bayesian_Estimation.srt
' h4 H8 Q- m! f4 l0 ]8 D │ │ │ 03_Bayesian_Estimation.txt. u( e- ]; E9 B! u$ |
│ │ │ 04_Bayesian_Prediction.mp4
; R/ x6 I7 d# w' p │ │ │ 04_Bayesian_Prediction.srt2 Y% Q& Z8 Q5 ?0 K7 K4 z* j6 U
│ │ │ 04_Bayesian_Prediction.txt
: F8 F' k. \. ? v. h% C, Z │ │ │ # u- V8 O) B* N) R/ a: {8 J: F
│ │ ├─19_Learning-_Parameter_Estimation_in_MNs( T5 F" z+ q; y. r
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.mp4
- {: _7 Q7 C8 l3 e │ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.srt: R& s Z& p' d Y) V8 v1 H, X
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.txt# F2 q1 e4 E3 Q: [# h
│ │ │
/ X# S& c* z* Y; {+ A │ │ ├─20_Structure_Learning& S" Y1 L' B9 b8 M
│ │ │ 01_Structure_Learning_Overview.mp4
# e2 L x! d9 r2 h5 g$ k │ │ │ 01_Structure_Learning_Overview.srt j! |. c. t$ a8 Y
│ │ │ 01_Structure_Learning_Overview.txt
5 Z9 L( n/ Y& l, r7 O │ │ │ 02_Likelihood_Scores.mp4: D$ W- |* ?: X
│ │ │ 02_Likelihood_Scores.srt- R$ m" t' m, Y+ S1 G3 v' g
│ │ │ 02_Likelihood_Scores.txt8 w, S: G2 h& o* K8 i7 D
│ │ │ 03_BIC_and_Asymptotic_Consistency.mp4: y* r* b6 R$ h! d4 t
│ │ │ 03_BIC_and_Asymptotic_Consistency.srt; R" F- ]" t. A; u5 z, l/ M
│ │ │ 03_BIC_and_Asymptotic_Consistency.txt6 R' @: G& g7 ?) p* f
│ │ │ 04_Bayesian_Scores.mp4
. U; ?* Q' { N( o │ │ │ 04_Bayesian_Scores.srt, F4 ^$ s2 D- K; `4 W* |7 G5 y
│ │ │ 04_Bayesian_Scores.txt; H! T8 }! ?' Y$ M+ F
│ │ │ 05_Learning_Tree_Structured_Networks.mp4
7 u' z9 ]( R% U. d, u9 l │ │ │ 05_Learning_Tree_Structured_Networks.srt4 {9 ~4 i; [# ]8 ~7 @/ i9 B
│ │ │ 05_Learning_Tree_Structured_Networks.txt0 ]0 u3 S2 [! `# ^# t1 {( r2 @
│ │ │ 06_Learning_General_Graphs-_Heuristic_Search.mp46 l3 Y9 {5 d( ]4 y
│ │ │ 06_Learning_General_Graphs-_Heuristic_Search.srt9 N1 B' S9 Y4 K' C6 H
│ │ │ 06_Learning_General_Graphs-_Heuristic_Search.txt
) S' [- |9 q* t n/ q$ n( t │ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.mp43 ~. r% H ? D1 H8 ^( b" Y7 }
│ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.srt
+ k6 r Z; v. L3 o! r │ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.txt* f$ B2 Q- B0 T& d d* o
│ │ │
! W# R9 L& p9 i8 o │ │ ├─21_Learning_With_Incomplete_Data
, _1 `: {, j% f3 v" z │ │ │ 01_Learning_With_Incomplete_Data_-_Overview.mp4; p8 o$ W9 g( _
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.srt
# ?; W, k+ [9 X3 ] │ │ │ 01_Learning_With_Incomplete_Data_-_Overview.txt) y- b7 i0 L0 q
│ │ │ 02_Expectation_Maximization_-_Intro.mp4, y* a: B; m. s
│ │ │ 02_Expectation_Maximization_-_Intro.srt
' U' ?! P* N, T0 i3 F0 E T │ │ │ 02_Expectation_Maximization_-_Intro.txt4 k$ @; W' d3 f* ~3 g' b
│ │ │ 03_Analysis_of_EM_Algorithm.mp4) E* h% _' q& Q3 S- E% b' s
│ │ │ 03_Analysis_of_EM_Algorithm.srt" B! o+ [1 t( p% h+ T* z) S
│ │ │ 03_Analysis_of_EM_Algorithm.txt8 t0 } W8 B; G) Z! T' M k
│ │ │ 04_EM_in_Practice.mp4
3 j7 A. g4 z; \ │ │ │ 04_EM_in_Practice.srt
8 g1 [1 Y6 }. x2 z2 [# w │ │ │ 04_EM_in_Practice.txt+ ?% m6 k$ v0 ]4 J; v
│ │ │ 05_Latent_Variables.mp4
% a! i+ A$ j' Y$ F6 [& i7 | │ │ │ 05_Latent_Variables.srt
( L3 J2 \4 G6 S9 n3 l3 o" a │ │ │ 05_Latent_Variables.txt& P3 J5 S) \! v" O- p6 ?
│ │ │
% g2 l' P( m. { │ │ ├─22_Learning-_Wrapup
) W6 S* I% R# w5 \' [. c │ │ │ 01_Summary-_Learning.mp4
/ P5 ]+ }8 W' E. G$ q │ │ │ 01_Summary-_Learning.srt# W( \$ A. D* y$ a- a' z' D
│ │ │ 01_Summary-_Learning.txt
- S& k* S9 C( f) C │ │ │ 7 T0 c2 e9 R9 B4 @5 O. R
│ │ └─23_Summary; x/ Y. w2 ^& P' v+ u
│ │ 01_Class_Summary.mp4
* S0 @+ p1 l2 A* n" }6 {- ~ │ │ 01_Class_Summary.srt" c6 P$ v$ y" L6 K8 l% K
│ │ 01_Class_Summary.txt
' b+ F y/ a/ } x1 M) \ │ │
; u. q/ t6 {" a. |0 l! M1 ]- ^% F │ └─Probabilistic Graphical Models - Stanford5 N/ |( k+ [+ S
│ 1 - 1 - Welcome! (05-35).mp4
6 ^7 W! e1 H) p# J$ H) ? │ 1 - 1 - Welcome! (05-35).srt1 w4 s6 b c, h" G, W( u
│ 1 - 2 - Overview and Motivation (19-17).mp41 S d& [2 t: u6 i5 B, \: f* n
│ 1 - 2 - Overview and Motivation (19-17).srt& @% `( F( D' z' {) k' c( z1 }
│ 1 - 3 - Distributions (04-56).mp4
% a) C2 X6 s) i' |- X& g │ 1 - 3 - Distributions (04-56).srt/ {5 g1 I& |2 U- n: R
│ 1 - 4 - Factors (06-40).mp4
$ D9 e, j( Q& ]; }+ z" k. a │ 1 - 4 - Factors (06-40).srt) O5 r0 M5 h8 ?/ D, d
│ 10 - 4 - Clique Trees and Independence (15-21).mp4
, R9 C. |3 u# O: b" q │ 10 - 4 - Clique Trees and Independence (15-21).srt
* a% a9 d8 A* P* | │ 10 - 5 - Clique Trees and VE (16-17).mp4
4 [4 I# d/ ?0 d0 o │ 10 - 5 - Clique Trees and VE (16-17).srt
7 |! ~; K! i0 V: P6 Y │ 10 - 6 - BP In Practice (15-38).mp45 a' s) [5 {, A2 U6 @
│ 10 - 6 - BP In Practice (15-38).srt6 M! Q6 A7 I+ s9 D0 b+ r1 u
│ 10 - 7 - Loopy BP and Message Decoding (21-42).mp4
7 Z- X3 c" [6 m+ P │ 10 - 7 - Loopy BP and Message Decoding (21-42).srt
* _5 u& y! ~( Y# L( k/ [; a │ 11 - 1 - Max Sum Message Passing (20-27).mp4, a2 p) E; d$ B: u2 c
│ 11 - 1 - Max Sum Message Passing (20-27).srt; R$ Y+ Y' p- @9 z5 w
│ 11 - 2 - Finding a MAP Assignment (3-57).mp4* O. X3 m3 `7 A
│ 11 - 2 - Finding a MAP Assignment (3-57).srt+ a* L5 r( m% O4 \8 o, y! @( `
│ 12 - 1 - Tractable MAP Problems (15-04).mp49 l N7 D! Y) G5 i
│ 12 - 1 - Tractable MAP Problems (15-04).srt
D) M" P5 T8 B1 V& l! j: F │ 13 - 1 - Simple Sampling (23-37).mp4
4 b- M' G% E$ ?$ X! z3 _7 s# ` │ 13 - 1 - Simple Sampling (23-37).srt9 V. w, f) S; m. J2 q
│ 13 - 2 - Markov Chain Monte Carlo (14-18).mp4
5 o: y* C! V0 g$ g. g- q$ {! g8 m │ 13 - 2 - Markov Chain Monte Carlo (14-18).srt9 j0 t; w( _ l9 e
│ 13 - 3 - Using a Markov Chain (15-27).mp4
/ N. c3 L( F- Q' T( p! J │ 13 - 3 - Using a Markov Chain (15-27).srt; l5 ^4 u0 x C; }. N: M0 W
│ 13 - 4 - Gibbs Sampling (19-26).mp4& u; [+ e9 K" G4 J& r9 r! a0 S
│ 13 - 4 - Gibbs Sampling (19-26).srt* j; R3 C6 j0 q, o0 v. H
│ 13 - 5 - Metropolis Hastings Algorithm (27-06).mp4: t, ^, A$ P! ?% n$ q S
│ 13 - 5 - Metropolis Hastings Algorithm (27-06).srt
2 |9 K8 Z) R* m/ x │ 14 - 1 - Inference in Temporal Models (19-43).mp4% V) p' C: R, ^
│ 14 - 1 - Inference in Temporal Models (19-43).srt6 n+ F# G8 T8 K% E" A2 U
│ 14 - 2 - Inference- Summary (12-45).mp4; |7 u8 f# ~" m @. o( n
│ 14 - 2 - Inference- Summary (12-45).srt5 k5 E: V8 _8 c" N
│ 15 - 1 - Maximum Expected Utility (25-57).mp4
/ L. O4 U( Q- S: n' c* j( j │ 15 - 1 - Maximum Expected Utility (25-57).srt+ P7 {( s9 H3 X% E% I N
│ 15 - 2 - Utility Functions (18-15).mp46 X4 d `1 t' Y' e2 O* P$ z" Y6 F
│ 15 - 2 - Utility Functions (18-15).srt
1 f& F7 P* n7 p2 C │ 15 - 3 - Value of Perfect Information (17-14).mp4# j! v# U- }7 K
│ 15 - 3 - Value of Perfect Information (17-14).srt
! b. l4 O5 o$ K# S5 i$ k5 g0 q │ 16 - 2 - Regularization- Cost Function (10-10).mp4
|* H2 s% s! M. _ │ 16 - 2 - Regularization- Cost Function (10-10).srt
2 W, X2 r, C* V- o │ 16 - 3 - Evaluating a Hypothesis (07-35).mp42 T% Y/ F0 `6 b: w1 v
│ 16 - 3 - Evaluating a Hypothesis (07-35).srt1 z+ F1 }1 A1 Q0 F. U$ h8 s5 a2 O
│ 16 - 5 - Diagnosing Bias vs Variance (07-42).mp4
, {# o: C0 v5 j; T │ 16 - 5 - Diagnosing Bias vs Variance (07-42).srt$ A+ H }. U% E8 ]6 v4 l# f5 w. i
│ 17 - 1 - Learning- Overview (15-35).mp47 b* B) A' H; ?, Z$ w- p) Q
│ 17 - 1 - Learning- Overview (15-35).srt
- D) X( e2 l0 ?) z/ ? │ 18 - 1 - Maximum Likelihood Estimation (14-59).mp4
( u3 ?' u" S. F0 t6 T2 Z │ 18 - 1 - Maximum Likelihood Estimation (14-59).srt
9 I. f6 l( |, c/ K │ 18 - 3 - Bayesian Estimation (15-27).mp4# ?7 a" u; k- S/ @' ~$ V
│ 18 - 3 - Bayesian Estimation (15-27).srt5 X" B# H; h6 g
│ 18 - 4 - Bayesian Prediction (13-40).mp4
8 V. k8 C$ w1 d! p8 l1 U │ 18 - 4 - Bayesian Prediction (13-40).srt. S, Z) g' g) I9 m5 F/ U( ]9 E
│ 2 - 1 - Semantics & Factorization (17-20).mp4
# m0 p8 g! a: [6 _: v. W │ 2 - 1 - Semantics & Factorization (17-20).srt
9 y6 U# s; Q7 @& [ │ 2 - 2 - Reasoning Patterns (09-59).mp4
5 |: W' L' K5 }5 q │ 2 - 2 - Reasoning Patterns (09-59).srt# ?' R4 ?7 }; T) S0 b4 o
│ 2 - 4 - Conditional Independence (12-38).mp4
' w0 d0 O; v. y9 s5 J& D% K* @ _' P │ 2 - 4 - Conditional Independence (12-38).srt" F+ ]8 W4 l: h G
│ 2 - 6 - Naive Bayes (09-52).mp4
& R) i0 k. S2 H3 A; i: F │ 2 - 6 - Naive Bayes (09-52).srt6 |$ O: G9 w. B8 n& }
│ 20 - 1 - Structure Learning Overview (5-49).mp4
8 p" T# G3 ?+ s0 r# [; {7 v │ 20 - 1 - Structure Learning Overview (5-49).srt9 G# F& o: Z: p' X" X( q
│ 20 - 2 - Likelihood Scores (16-49).mp4+ u( I7 F O+ |
│ 20 - 2 - Likelihood Scores (16-49).srt& g7 A1 Y% S$ B
│ 20 - 4 - Bayesian Scores (20-35).mp4
) H+ H: X! _9 s( p$ b* v. i │ 20 - 4 - Bayesian Scores (20-35).srt
, q; s/ h% T! t) ~; d) ? │ 21 - 3 - Analysis of EM Algorithm (11-32).mp4
* t! z: k, ~9 ?8 a% C/ C! V3 t │ 21 - 3 - Analysis of EM Algorithm (11-32).srt
n( z: R1 M4 W4 f. Y/ G │ 21 - 4 - EM in Practice (11-17).mp48 e+ C8 v0 O! ^4 L% h+ |# f9 e0 s" o
│ 21 - 4 - EM in Practice (11-17).srt
/ f$ \9 q* F; ]6 h2 Q │ 21 - 5 - Latent Variables (22-00).mp4
3 }4 x' U& H! t5 m. ?8 a │ 21 - 5 - Latent Variables (22-00).srt8 @5 ]* w3 y# y% b
│ 22 - 1 - Summary- Learning (20-11).mp44 W4 m+ w1 e; C6 v
│ 22 - 1 - Summary- Learning (20-11).srt7 \8 G" h/ e4 b7 N# t0 ~
│ 23 - 1 - Class Summary (24-38).mp4
: q% C. w* x1 R │ 23 - 1 - Class Summary (24-38).srt
0 `' b6 b4 c! ^! m! A3 u; n" s │ 3 - 1 - Overview of Template Models (10-55).mp45 X3 Q% R, o Y0 `, D
│ 3 - 1 - Overview of Template Models (10-55).srt" _* Z9 g1 O6 }
│ 3 - 2 - Temporal Models - DBNs (23-02).mp4
) W! M$ E' S1 [ d │ 3 - 2 - Temporal Models - DBNs (23-02).srt; Y( A; J$ @( E
│ 3 - 3 - Temporal Models - HMMs (12-01).mp4
7 E$ A! p9 K/ k/ d! d │ 3 - 3 - Temporal Models - HMMs (12-01).srt$ _6 \: B" O) p- K9 e/ u
│ 3 - 4 - Plate Models (20-08).mp4
# f# E+ I) O- ^" u, { x │ 3 - 4 - Plate Models (20-08).srt6 c7 Z2 N+ H C5 {
│ 4 - 1 - Basic Operations (13-59).mp4# A) J m% c+ ?( A: _! z
│ 4 - 1 - Basic Operations (13-59).srt
* \. e6 y0 j0 {7 }+ L& e │ 4 - 2 - Moving Data Around (16-07).mp4
. e& a! Z. {/ y% l │ 4 - 2 - Moving Data Around (16-07).srt
6 Y9 i, i3 |6 x5 _ │ 4 - 3 - Computing On Data (13-15).mp44 b/ U3 \/ J& H0 B! m( ~$ o
│ 4 - 3 - Computing On Data (13-15).srt% T9 o) f8 k. D8 S7 l" y$ z
│ 4 - 4 - Plotting Data (09-38).mp4
2 h0 J: Y T7 z+ r$ d7 t │ 4 - 4 - Plotting Data (09-38).srt/ }" W B0 s7 x
│ 4 - 6 - Vectorization (13-48).mp4/ m: M$ r2 y% h6 n3 C/ Y# t# H
│ 4 - 6 - Vectorization (13-48).srt y6 Z# d8 u9 V6 Q# P
│ 5 - 1 - Overview- Structured CPDs (08-00).mp4& k& U: O3 Y0 X1 d, s! C! A
│ 5 - 1 - Overview- Structured CPDs (08-00).srt
( f( L" \: t& u │ 5 - 2 - Tree-Structured CPDs (14-37).mp4
' L% Z& M' Z2 o8 u5 `8 H7 P2 B │ 5 - 2 - Tree-Structured CPDs (14-37).srt/ R' s z& G9 e" r" \1 w" h9 f8 X+ N
│ 5 - 4 - Continuous Variables (13-25).mp4! x/ \6 O4 s0 S' k# w( h+ G
│ 5 - 4 - Continuous Variables (13-25).srt
$ m* G% @( D: O4 `3 `% p S │ 6 - 1 - Pairwise Markov Networks (10-59).mp4% ~5 G0 Z3 G9 X+ _) d
│ 6 - 1 - Pairwise Markov Networks (10-59).srt
1 I6 G' f# U; ~" e( K1 C2 ~ │ 6 - 2 - General Gibbs Distribution (15-52).mp4; |, Z- \6 i. c/ @5 p$ R
│ 6 - 2 - General Gibbs Distribution (15-52).srt
! Z2 U. ^* V! w3 B+ Y2 H8 m- \5 M │ 6 - 3 - Conditional Random Fields (22-22).mp4- d7 P. v6 L8 w! E) \- J Z
│ 6 - 3 - Conditional Random Fields (22-22).srt
7 Y; N1 I2 J6 u( k9 j I8 { │ 6 - 5 - I-maps and perfect maps (20-59).mp4
. i8 n! h8 P3 d* N │ 6 - 5 - I-maps and perfect maps (20-59).srt. s0 h& z$ d$ a
│ 6 - 6 - Log-Linear Models (22-08).mp4
9 q) u/ s# H$ T. D' C- A: f │ 6 - 6 - Log-Linear Models (22-08).srt& s7 A' t9 o! h. y
│ 7 - 1 - Knowledge Engineering (23-05).mp4' ^ m b5 s1 ~# H
│ 7 - 1 - Knowledge Engineering (23-05).srt
- s+ S! h2 ]# T4 y4 s0 ? │ 8 - 2 - Overview- MAP Inference (09-42).mp46 Q) ^# K! F& G& h2 N1 K1 h+ y: E/ N
│ 8 - 2 - Overview- MAP Inference (09-42).srt( x4 t( E0 W" K' F2 m' q
│ 8 - 3 - Variable Elimination Algorithm (16-17).mp4
% A1 z. t$ r: z. M; `/ `, } │ 8 - 3 - Variable Elimination Algorithm (16-17).srt
$ E8 U, p% S5 Y$ [* v$ ^3 Q │ 8 - 6 - Finding Elimination Orderings (11-58).mp4
; e5 ^; [7 ?, D0 f( d │ 8 - 6 - Finding Elimination Orderings (11-58).srt
( X# c/ U' Z% w) } │ 9 - 1 - Belief Propagation (21-21).mp4" {+ z% X: r5 c) ^
│ 9 - 1 - Belief Propagation (21-21).srt
- r4 S, h3 l, L$ E │ 9 - 2 - Properties of Cluster Graphs (15-00).mp4
/ Z) g( p! @1 E; E. E! Q: R, o, @ │ 9 - 2 - Properties of Cluster Graphs (15-00).srt
9 y- r; e/ o0 K, c │ PGM-Programming_Assignment_1.zip* v' V, a' X- }) K4 D. `
│ PGM-Programming_Assignment_2.zip8 H( |# u- P* A4 i
│ PGM-Programming_Assignment_3.zip0 g8 A+ N: }3 U) h5 S
│ PGM-Programming_Assignment_4.zip
" q! S2 M- {' h# N6 h; L. x ? │ PGM-Programming_Assignment_5.zip$ c6 p# m: _4 _" x! A
│ PGM-Programming_Assignment_6.zip
+ ~2 E- M2 O9 ~; C9 t3 ?0 t │ PGM-Programming_Assignment_7.zip
; S- s# }) ?& p, u │ PGM-Programming_Assignment_8.zip% K! Q6 ]# v# Y
│ PGM-Programming_Assignment_9.zip
) ^' V, h& o7 G% C/ \+ r │ 下载说明.txt D0 T. P1 R1 m1 ~' R2 N+ @2 R
│ 关注我们.png
; o8 F3 w% d F3 y9 z │ 攻城狮论坛=网络技术+编程视频.url
5 C H# Q* |. K$ ^4 g │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt T6 W' P( `3 D$ v: c
│
9 F+ F( F! ~+ K% {/ A. I( C! a$ z; f ├─031_台大概率 ?1 [! ^2 n+ k3 V V& s
│ └─031_台大概率; i* ]0 k: _! |8 B
│ ├─prob-001. h: i: j- G3 F4 J0 z
│ │ ├─01_Week_1
9 K7 Q/ q/ c6 |" @: m% d │ │ │ 01_1-0.mp4" t$ ?, E9 ^( F2 H6 J
│ │ │ 02_1-1_17-41.mp4% M% v+ i! O' o
│ │ │ 02_1-1_17-41.pdf
) a, M% E1 z3 q+ `/ M7 L │ │ │ 02_1-1_17-41_0_.pdf
% ?% L* t& V" }5 h; n* K3 R │ │ │ 02_1-1_17-41_1_.pdf, h* ]* a7 `7 J+ }9 R3 k
│ │ │ 02_1-1_17-41_2_.pdf
5 }& E% j d) j, b: V3 G │ │ │ 03_1-2.a__11-46.mp4
. v8 Z6 J, V2 N ?0 p │ │ │ 04_1-2.b__09-40.mp4
& g, d5 ?0 e$ O) D- T3 j' ~* k# c5 A │ │ │ 05_1-3.a__11-24.mp44 f c4 }+ B m
│ │ │ 06_1-3.b__16-36.mp4
# J" O" n5 l: s# O, H │ │ │
& m- ~' h7 K5 }1 N │ │ ├─02_Week_2
2 ~. _6 P( v1 w1 w │ │ │ 01_2-0_15-32.mp45 z* G: _4 b E j/ e" h! t
│ │ │ 02_2-1.a__16-09.mp4
9 W. N7 I) f; o) A │ │ │ 02_2-1.a__16-09.pdf
) q( o* r) }( T Y6 L& D6 N9 W │ │ │ 02_2-1.a__16-09_0_.pdf. v" L7 v, ?: C$ F5 H. M$ {
│ │ │ 02_2-1.a__16-09_1_.pdf; W) b$ M8 g. e- Q/ C
│ │ │ 02_2-1.a__16-09_2_.pdf( K: s( F2 _: o2 ]1 v' d
│ │ │ 03_2-1.b__16-06.mp4
3 i. Q' Z" o6 V' D │ │ │ 04_2-1.c__10-07.mp4
: ]( j4 \! k( b1 ^- S6 ~5 U, N │ │ │ 05_2-2.a__10-41.mp4
6 | \6 D' N& ^3 h! ] │ │ │ 06_2-2.b__12-43.mp4
/ @+ B2 t( q. ^7 \. s' @. }9 b │ │ │ 07_2-2.c__16-02.mp4: t$ k7 z7 V0 r
│ │ │ - _4 _6 H2 z( O- N8 h+ \# f
│ │ ├─03_Week_3
( [1 Q0 V/ m9 V/ Q: I6 c. R, M3 W │ │ │ 01_3-0_16-46.mp4' M2 H0 o1 C; _3 ?- |/ ] G& k
│ │ │ 02_3-1.a___09-12.mp4
0 T3 Z5 [: q. z! ]; q0 R │ │ │ 02_3-1.a___09-12.pdf2 I. F l; W4 P9 h) a3 f! J# k
│ │ │ 02_3-1.a___09-12_0_.pdf% q2 X7 P$ H! p+ M
│ │ │ 02_3-1.a___09-12_1_.pdf
/ `& V" \: m1 H: z( E% k( I$ [ │ │ │ 02_3-1.a___09-12_2_.pdf
4 E" ^- K( a$ F6 ]7 V, q, \ │ │ │ 03_3-1.b__10-35.mp4
9 J, a! H) ^. U& [- \/ W │ │ │ 04_3-2_08-47.mp4! t& t" \4 l# q, V
│ │ │ 05_3-3.a__16-57.mp4
& ]8 B- K8 E% F. I$ i │ │ │ 06_3-3.b__12-58.mp4% E4 O3 x# c% H4 C9 W7 u: j( `
│ │ │
& R2 W& H- ^/ L# z7 B& i: Q │ │ ├─04_Week_4
4 P: ^% ?- j* Z" F& f$ Y( p │ │ │ 01_4-0_17-33.mp4& c. g5 s( O5 I7 b! p
│ │ │ 02_4-1.a__13-53.mp40 ]3 z% \7 T# m o1 Z* J( Z
│ │ │ 02_4-1.a__13-53.pdf
( i6 f( ~6 I5 s" M& c │ │ │ 02_4-1.a__13-53_0_.pdf
' Y: D, j8 Y. {& |; C9 \ │ │ │ 02_4-1.a__13-53_1_.pdf
: x. X5 N+ B! v0 i" |; ]2 m+ e* W │ │ │ 02_4-1.a__13-53_2_.pdf) p5 }' c9 w' S/ d3 h4 B
│ │ │ 03_4-1.b__14-43.mp41 _+ r2 d/ k8 o' N1 \" r; J
│ │ │ 04_4-1.c__5-18.mp4
9 Q7 z% d. W( b, w │ │ │ 05_4-2.a_CDF__9-48.mp45 }5 G4 f* D, s4 }6 K3 |
│ │ │ 06_4-2.b_CDF__8-59.mp4
9 K& N/ Q5 v9 {' H8 j. r3 d │ │ │ 07_4-2.c_CDF__9-00.mp4
0 M" _: W- v& A, e$ j9 c │ │ │ 08_4-3_PMF_11-26.mp4
2 H, L5 R1 Z+ M# i: M/ W │ │ │ 09_4-4.a_I__14-41.mp43 u# b" ?: j5 R% @/ |( v. o) K) c. q0 ]
│ │ │ 10_4-4.b_I__8-47.mp4
- a( q; I) F4 f9 b, \/ Q6 I9 X5 [ │ │ │ * c1 p; y" E% P- Z( [
│ │ ├─05_Week_5
+ w8 G$ D$ J( |8 f. s8 W │ │ │ 01_5-0_14-09.mp4
4 v% P- X5 C v. E/ M5 C0 s │ │ │ 02_5-1.a_II__10-36.mp4
% s# w4 p6 D. B: E1 o │ │ │ 02_5-1.a_II__10-36.pdf
7 ?; ^1 N( p, H; e. z9 d │ │ │ 02_5-1.a_II__10-36_0_.pdf
4 O- k) n6 E' R9 u- K7 y │ │ │ 02_5-1.a_II__10-36_1_.pdf; n& V* S! o4 m9 U7 H4 q# u
│ │ │ 02_5-1.a_II__10-36_2_.pdf
, @8 _5 y( B; ~. H) h3 h │ │ │ 03_5-1.b_II__12-06.mp4' F9 k" d0 h( f
│ │ │ 04_5-1.c_II__20-28.mp4
! M4 R1 h% w) k' B( N! h T9 R │ │ │ 05_5-2_PDF_18-56.mp4
2 D6 i' Y8 Z7 f. e3 s$ K9 ]/ L │ │ │ 06_5-3_I_18-12.mp4
+ V) p' g; F% f V) F5 t) Q ^ │ │ │
9 t" ?6 X& _& H6 {# x │ │ ├─06_Week_6
% Z' ]3 o3 J- u3 T8 H │ │ │ 01_6-0_10-13.mp4
+ H- A7 Y+ S- k& L2 U │ │ │ 02_6-1.a_II__15-25.mp4
/ c& G( l( n Z! `& I" k' R5 [ │ │ │ 02_6-1.a_II__15-25.pdf
W& l" z d' d │ │ │ 02_6-1.a_II__15-25_0_.pdf
# z4 N$ q* {. u& T │ │ │ 02_6-1.a_II__15-25_1_.pdf( O) W0 T, E8 j8 I/ ~2 U* B* J& s! b
│ │ │ 02_6-1.a_II__15-25_2_.pdf
- F$ T( B! {' x! h* W+ ] │ │ │ 03_6-1.b_II__16-08.mp4
- I+ l$ i; A8 B/ b% h │ │ │ 04_6-1.c_II__17-16.mp4$ z Q* m2 a) V/ N, G+ Z
│ │ │ 05_6-1.d_II__5-40.mp49 b$ m; Y/ m2 {) Q9 i9 a
│ │ │ 06_6-2.a_I__16-35.mp4
6 O2 [7 m$ k6 R' ^: @2 X │ │ │ 07_6-2.b_I__10-41.mp4
, W$ c ?" H4 k5 q4 ~; b │ │ │ 08_6-2.c_I__16-44.mp4
, J2 T; `* D( ?. L! @ │ │ │ 09_6-2.d_I__14-30.mp4
3 J' x) F( i5 P" v7 v │ │ │
8 |# V7 U6 ~" J │ │ ├─07_Week_7" B7 B& V1 {; Z% n
│ │ │ 01_7-0.mp4
) \* F5 ?# R; Y1 T0 I$ J │ │ │ 02_7-1.a_II__14-31.mp4
/ ?. V {2 ?6 H │ │ │ 02_7-1.a_II__14-31.pdf" G+ ^6 a$ c* b: \' b
│ │ │ 02_7-1.a_II__14-31_0_.pdf
5 O7 _+ U/ e, u0 e$ ]8 }9 V$ M │ │ │ 02_7-1.a_II__14-31_1_.pdf# \# b* D( q- Y, _4 R9 R; ^1 E+ N2 J
│ │ │ 02_7-1.a_II__14-31_2_.pdf
& q0 u1 a2 P8 ]! ` │ │ │ 03_7-1.b_II__13-07.mp4
. ^' Q$ q/ H/ g │ │ │ 04_7-2.a-___10-35.mp48 {+ L2 P2 _$ C* C& W8 ?( G
│ │ │ 05_7-2.b-___08-42.mp4# d5 [6 R6 n) a, W
│ │ │ 06_7-3.a-___15-07.mp4+ m9 U x5 t% e
│ │ │ 07_7-3b-___19-20.mp4
$ I6 U$ c" v. c: O+ g │ │ │ ' u, ?0 R7 `: v: E$ |
│ │ ├─08_Week_8
: z- Y. Z7 }3 l1 U: J% y4 ] │ │ │ 01_8-0.mp43 }) W( A+ v2 H- x6 `% [
│ │ │ 02_8-1.a__14-36.mp4$ b; I/ v) l( V2 N7 v6 u
│ │ │ 02_8-1.a__14-36.pdf
# p3 d/ q6 k5 l- ?; k3 E │ │ │ 02_8-1.a__14-36_0_.pdf7 m2 |& y7 U( W+ g4 r0 U8 \
│ │ │ 02_8-1.a__14-36_1_.pdf6 A. }* \4 p1 O' M
│ │ │ 02_8-1.a__14-36_2_.pdf
, w/ h* P% [% u │ │ │ 03_8-1.b__15-05.mp46 {( h* j0 U. {& g+ @
│ │ │ 04_8-1.c__17-00.mp41 P1 e8 z8 O5 z8 ], D
│ │ │ 05_8-1.d__11-18.mp4; g. c1 o4 p8 j5 j
│ │ │ 06_8-2_12-32.mp4
4 L4 L, O! X: A+ _9 ?% p' J │ │ │ 07_8-3.a__10-06.mp4
1 j! n3 C5 Z( ^6 U, n0 L% e │ │ │ 08_8-3.b__17-27.mp4( T/ X8 Y$ @' Y( n
│ │ │ . Z0 T6 A# n" K! H
│ │ └─09_Week_9+ c' L3 s6 {% c# z3 U+ |2 ?+ b
│ │ 01_9-1.a__11-18.mp4
7 i3 _6 Q3 F+ Y' n+ \/ ]4 l, r │ │ 01_9-1.a__11-18.pdf1 j4 W& ]! G- H V5 ~ ^; p
│ │ 01_9-1.a__11-18_0_.pdf9 d' u5 |9 N9 n; w/ g6 h
│ │ 01_9-1.a__11-18_1_.pdf
/ F; i8 s: K# W/ c0 \ │ │ 01_9-1.a__11-18_2_.pdf2 H& x9 Z" f0 Y. N2 m
│ │ 02_9-1.b__13-49.mp4% Y9 F$ a |; C3 X$ [
│ │ 03_9-2.aMGF__10-17.mp4! ]" `" k8 N q% K( w! m
│ │ 04_9-2.bMGF__14-06.mp4! [# u. I. d8 @. v0 \# c- P: t# J
│ │ 05_9-2.cMGF__15-53.mp4
5 u# y; t6 b( f │ │ 06_9-3.a__10-35.mp42 z! i2 B: n0 m) Y6 w U/ c( C; r
│ │ 07_9-3.b__13-01.mp4 f o' z0 ~* e; B( w
│ │ 08_9-4.a-__16-45.mp4
6 B! Y# j. f# \ │ │ 09_9-4.b-__17-19.mp40 V- w! O/ ?. d( I1 {. N4 I: O
│ │ + D& c+ w1 S* q; c: |0 Y5 D5 q
│ └─概率-台大9 O; G2 P4 O; o6 A
│ │ 02-8.R" k" q0 M9 e9 f8 w$ v" w
│ │
. ]# ]3 {+ m$ q$ L& b) C" @ │ ├─1! [0 K0 Z; E7 h. O/ f; z/ b' o, Z
│ │ Benson_Coursera_Week_1_簡.pdf* i* j+ g& ~& f7 L
│ │ Benson_Coursera_Week_1_繁.pdf1 @# y" E( ` V0 ]
│ │ Benson_Coursera_Week_1_繁空.pdf! c2 W# w& [& Y- m/ O* F
│ │ 2 - 1 - 1-0：咱們先聊聊，這是門什麼樣的課呢-.mp4
0 s* A. A- a8 O2 t( p# }* \ │ │ 2 - 2 - 1-1：機率概論 (17-41).mp4
: _8 b q5 w) N- _) y' F* @ │ │ 2 - 3 - 1-2.a：集合論 (上) (11-46).mp4- k A8 j& e( Z% w; c5 B
│ │ 2 - 4 - 1-2.b：集合論 (下) (09-40).mp4
6 ]* x' u7 X9 W! }( |9 B5 r │ │ 2 - 5 - 1-3.a：機率名詞 (上) (11-24).mp4
7 D6 n! X# h: V# e& s │ │ 2 - 6 - 1-3.b：機率名詞 (下) (16-36).mp42 e! D* h- G2 M7 Q1 `
│ │
5 _5 s3 B( i9 u7 C8 a3 `: H │ ├─22 B$ v% E3 ~) S6 u4 ?
│ │ Benson_Coursera_Week_2_簡.pdf
6 I s% N5 h4 X% k$ ? │ │ Benson_Coursera_Week_2_簡空.pdf% n- z6 \. [& _
│ │ Benson_Coursera_Week_2_繁.pdf2 ^ c0 k" q* Y& V0 `
│ │ Benson_Coursera_Week_2_繁空.pdf6 n# j. n: D6 b7 _. a' X+ S8 K
│ │ 3 - 1 - 2-0：咱們聊聊，是學習，還是應付- (15-32).mp4
8 Y+ j9 z& R2 n0 L7 [9 }2 j8 ]& x │ │ 3 - 2 - 2-1.a：機率公理性質 (上) (16-09).mp4$ r6 N: k. s+ `6 I- L+ I
│ │ 3 - 3 - 2-1.b：機率公理性質 (中) (16-06).mp4
; e& g. k* O! m2 q% ]0 E# Q5 z │ │ 3 - 4 - 2-1.c：機率公理性質 (下) (10-07).mp4! d; Z- Z) c1 I1 ]/ V1 r
│ │ 3 - 5 - 2-2.a：條件機率 (上) (10-41).mp4+ w& h0 L" T) [& q: J* K$ m0 [
│ │ 3 - 6 - 2-2.b：條件機率 (中) (12-43).mp4# [( B% W% t' U
│ │ 3 - 7 - 2-2.c：條件機率 (下) (16-02).mp4- z# w+ R5 b) i/ ?5 E5 c
│ │ - w; i( f; S! @9 B- X: Z0 M
│ ├─3
! z3 l# ]# D6 r; {/ K │ │ Benson_Coursera_Week_3_簡.pdf# Q- B8 U6 E6 f* p. W
│ │ Benson_Coursera_Week_3_簡空.pdf
) g% F( `0 Q- d: _% D- r │ │ Benson_Coursera_Week_3_繁.pdf6 V- @- a6 B5 b* @
│ │ Benson_Coursera_Week_3_繁空.pdf
! b. N8 j- [; X9 x: P# f2 e. G │ │ 4 - 1 - 3-0：咱們聊聊，常見的錯誤？學習的關鍵？ (16-46).mp49 i w y; M5 a
│ │ 4 - 2 - 3-1.a：機率的獨立性 (上) (09-12).mp4
) b# d! P4 {, i: ? │ │ 4 - 3 - 3-1.b：機率的獨立性 (下) (10-35).mp4' r+ A* T' d& l1 n! U( X
│ │ 4 - 4 - 3-2：圖解繁複機率 (08-47).mp4
) y e8 W/ k( f7 ~0 ~ │ │ 4 - 5 - 3-3.a：數數算機率 (上) (16-57).mp4
8 D5 Q }% H, _ │ │ 4 - 6 - 3-3.b：數數算機率 (下) (12-58).mp4. f6 [( a2 R U$ d& [% Y0 D5 A6 Z
│ │
" k- Z! M# ^: Z │ ├─4 ^: V7 D" v2 c8 N
│ │ Benson_Coursera_Week_4_簡.pdf
+ J6 S8 J0 F4 h# B │ │ Benson_Coursera_Week_4_簡空.pdf- G5 ~; P) W& b. M& I/ e
│ │ Benson_Coursera_Week_4_繁.pdf
" h5 N9 d: b, {6 i │ │ Benson_Coursera_Week_4_繁空.pdf1 E) k& s/ l: G5 K
│ │ 5 - 1 - 4-0：咱們聊聊，如何幫自己面對未來挑戰？ (17-33).mp4
. \2 I! }$ d I* o# Y │ │ 5 - 10 - 4-4.b：離散機率分佈 I (下) (8-47).mp4
+ `% a" R+ U7 Y5 B# e │ │ 5 - 2 - 4-1.a：隨機變數 (上) (13-53).mp4
: U7 J& A* o6 X; i( A4 m) D │ │ 5 - 3 - 4-1.b：隨機變數 (中) (14-43).mp4
0 _ G2 ]/ G2 Q2 o# c │ │ 5 - 4 - 4-1.c：隨機變數 (下) (5-18).mp4
7 h {6 U: S3 ~# P7 ?9 _6 n │ │ 5 - 5 - 4-2.a：累績分佈函數 CDF (上) (9-48).mp4
& \. c S# m7 k │ │ 5 - 6 - 4-2.b：累績分佈函數 CDF (中) (8-59).mp4. x* {+ L- a; ?7 u* _6 C0 _# s: s
│ │ 5 - 7 - 4-2.c：累績分佈函數 CDF (下) (9-00).mp4/ E5 `5 t7 x" \
│ │ 5 - 8 - 4-3：機率質量函數 PMF (11-26).mp4' ?+ {& }4 I4 ^) M y6 t9 D' D
│ │ 5 - 9 - 4-4.a：離散機率分佈 I (上) (14-41).mp4
& C- _) A' x+ i │ │ * C; n( p, u% y* ?) m
│ ├─5
. M7 }6 X( P4 u4 I │ │ Benson_Coursera_Week_5_簡.pdf; {9 e- k* }$ [, U# ~4 k+ N5 e
│ │ Benson_Coursera_Week_5_簡空.pdf- B% F9 {/ P% x; d/ g `
│ │ Benson_Coursera_Week_5_繁.pdf) u; b9 _7 b$ ^
│ │ Benson_Coursera_Week_5_繁空.pdf
7 M$ K. ] i7 Z8 f; a) Y% E9 v │ │ 6 - 1 - 5-0：咱們聊聊，願你夜夜好眠到天明！ (14-09).mp4
) M, D/ w. q: D3 g │ │ 6 - 2 - 5-1.a：離散機率分佈 II (上) (10-36).mp4
Q) f7 s5 k8 r" Q( D( v) R │ │ 6 - 3 - 5-1.b：離散機率分佈 II (中) (12-06).mp4
& b5 }8 F7 q' ^9 L+ `# j$ e1 D │ │ 6 - 4 - 5-1.c：離散機率分佈 II (下) (20-28).mp4* ]: B& ~ D/ i+ d: E) i7 d
│ │ 6 - 5 - 5-2：機率密度函數 PDF (18-56).mp44 r' s+ a& {3 x
│ │ 6 - 6 - 5-3：連續機率分佈 I (18-12).mp4
' s* ]6 A& @! A- H2 g" l! R# ? │ │
9 y& s: |( \1 P5 S) e │ ├─6
4 N' V. R1 N; @3 \" L │ │ Benson_Coursera_Week_6_簡.pdf$ u4 c8 Q) G# k$ `$ _' u
│ │ Benson_Coursera_Week_6_簡空.pdf5 H0 P }; \" h f, k
│ │ Benson_Coursera_Week_6_繁.pdf
# {# A0 J6 o9 `) m │ │ Benson_Coursera_Week_6_繁空.pdf, x1 n1 S3 T- r$ r, s' Z- [
│ │ 7 - 1 - 6-0：咱們聊聊，成功者的條件是什麼？ (10-13).mp4
+ a8 K" o' T8 s' n% m! ^* t" ? │ │ 7 - 2 - 6-1.a：連續機率分佈 II (上) (15-25).mp4
' N' o1 o# I' P1 w! W( U. q# s1 i │ │ 7 - 3 - 6-1.b：連續機率分佈 II (中) (16-08).mp4
/ `. j( W6 }% t, ^; b" E7 C J │ │ 7 - 4 - 6-1.c：連續機率分佈 II (下) (17-16).mp4* G6 D0 t& _) N
│ │ 7 - 5 - 6-1.d：連續機率分佈 II (末) (5-40).mp42 D7 f2 ?# K; t% ]' z0 ?
│ │ 7 - 6 - 6-2.a：期望值 I (上) (16-35).mp4
# }: Q5 i: n; x/ F; Z. C │ │ 7 - 7 - 6-2.b：期望值 I (中) (10-41).mp4, l1 O( Z/ `/ F$ v) p7 `# V
│ │ 7 - 8 - 6-2.c：期望值 I (下) (16-44).mp4% L5 z6 _* X$ g1 D& u) Y* v& t# \, b5 H
│ │ 7 - 9 - 6-2.d：期望值 I (末) (14-30).mp4 q+ ^' L( z2 V) A4 [: {
│ │ 8 }6 Y; `# _' |
│ ├─7
/ v- L0 K# g$ T4 q2 V │ │ Benson_Coursera_Week_7_簡.pdf% ?+ z& M: u# m% T4 R
│ │ Benson_Coursera_Week_7_簡空.pdf. k; j/ Y! z: ]9 j% z! {
│ │ Benson_Coursera_Week_7_繁.pdf
9 T# D+ U* ], l' J │ │ Benson_Coursera_Week_7_繁空.pdf
5 T y3 i, ]* K( X, \/ U │ │ 8 - 1 - 7-0：咱們聊聊，每天都在忙，忙的有用嗎？.mp4
+ ^, _$ C* k; a$ D1 a5 [ │ │ 8 - 2 - 7-1.a：期望值 II (上) (14-31).mp4
5 ^; Y" i! O0 \ │ │ 8 - 3 - 7-1.b：期望值 II (下) (13-07).mp4% H; N1 v8 t. L7 C# R
│ │ 8 - 4 - 7-2.a- 隨機變數之函數 (上) (10-35).mp4
" t+ L N5 I& C$ a' D5 \& c │ │ 8 - 5 - 7-2.b- 隨機變數之函數 (下) (08-42).mp48 b# r' s& A4 C9 h
│ │ 8 - 6 - 7-3.a- 條件機率分佈與失憶性 (上) (15-07).mp4
4 z* M7 q8 F2 n/ e │ │ 8 - 7 - 7-3b- 條件機率分佈與失憶性 (下) (19-20).mp4, R s, G4 d) @# U4 Q" N! W$ o I
│ │ 5 A& ^6 @( D' Y4 T, ^+ q2 E
│ ├─8
0 P. s! S7 y X5 s │ │ Benson_Coursera_Week_8_簡.pdf
- z) m- q" D' M │ │ Benson_Coursera_Week_8_簡空.pdf
$ Z( {1 V' N! B- ?2 Z7 x+ R! S5 @ │ │ Benson_Coursera_Week_8_繁.pdf
0 q" O: o0 ]% v │ │ Benson_Coursera_Week_8_繁空.pdf o1 r$ m0 g# T: h% @1 I9 `, G
│ │ 9 - 1 - 8-0：咱們聊聊，如何探索有意義的人生？.mp4
, [% o) s! b5 K) y │ │ 9 - 2 - 8-1.a：聯合機率分佈 (上) (14-36).mp4
5 |& B1 h, l1 g4 f/ B/ \$ T │ │ 9 - 3 - 8-1.b：聯合機率分佈 (中) (15-05).mp4
% j% y' z3 [( L │ │ 9 - 4 - 8-1.c：聯合機率分佈 (下) (17-00).mp4$ S5 t: X, A: `- M/ a; N
│ │ 9 - 5 - 8-1.d：聯合機率分佈 (末) (11-18).mp42 ~" _+ Z6 r% f0 g; l; S
│ │ 9 - 6 - 8-2：邊際機率分佈 (12-32).mp4
( B4 ^- l* ?( n' B │ │ 9 - 7 - 8-3.a：雙變數期望值 (上) (10-06).mp4. r6 q# [% n" `
│ │ 9 - 8 - 8-3.b：雙變數期望值 (下) (17-27).mp4: r2 p' }4 L8 L+ X4 S
│ │
- o: x2 [9 K# y; y │ └─9
) l" f" U3 `' C3 V% e9 d │ Benson_Coursera_Week_9_簡.pdf5 f# \! a, @: p8 I, }8 h
│ Benson_Coursera_Week_9_簡空.pdf
3 S( Z9 l5 o) j9 g! l │ Benson_Coursera_Week_9_繁.pdf; x$ f; |! `3 `( T9 P2 L9 C- B
│ Benson_Coursera_Week_9_繁空.pdf/ F. F" I: p2 n7 r$ e
│ 10 - 1 - 9-1.a：隨機變數之和 (上) (11-18).mp49 \( {' L8 U1 w, B: f) @
│ 10 - 2 - 9-1.b：隨機變數之和 (下) (13-49).mp4
# |. p- {- V0 ?- N2 h/ t │ 10 - 3 - 9-2.a：MGF (上) (10-17).mp4
- m1 p' o/ r0 X F* c$ U │ 10 - 4 - 9-2.b：MGF (中) (14-06).mp4
b" I) q0 `- Z0 y! ]6 Z T │ 10 - 5 - 9-2.c：MGF (下) (15-53).mp4) O6 K8 D$ X* N+ ^
│ 10 - 6 - 9-3.a：多個隨機變數和 (上) (10-35).mp46 }4 M- |8 Z" V6 ^( T$ i1 U6 X& ~0 z8 h
│ 10 - 7 - 9-3.b：多個隨機變數和 (下) (13-01).mp4# f$ A, d/ C6 r* B1 E% A1 ?
│ 10 - 8 - 9-4.a：中央極限定理-萬佛朝宗 (上) (16-45).mp4
) [9 T7 ^0 X4 s" J! [ │ 10 - 9 - 9-4.b：中央極限定理-萬佛朝宗 (下) (17-19).mp49 U8 F8 d) M% b2 _8 x- v+ @
│
2 u; A0 L4 }$ p4 q ├─036_Coding the Matrix
, A. m1 _4 f, s& h │ └─036_Coding the Matrix c% n8 A) k+ H( A% n5 j5 P6 C( n- G
│ └─matrix-0026 B3 A- p7 c% C& c
│ │ 下载说明.txt9 C; ]' N" e# L2 ?
│ │ 关注我们.png
9 ?' k5 e/ G1 s8 i0 F │ │ 攻城狮论坛=网络技术+编程视频.url4 b, }/ Q) m+ Q9 c! M5 ~: G4 s0 g
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
5 @+ H% n. d/ z- k8 P% \+ A │ │ 2 d* p& k+ f7 R/ ~3 R* a7 \$ w; d
│ ├─01_Week_0-_The_Function_and_the_Field
% L$ M* Z' r, M │ │ 01_Course_Introduction_Part_1__9-53.mp4
) a6 l, p' y6 L1 n' F │ │ 01_Course_Introduction_Part_1__9-53.srt
* b. U, e# K3 A- K$ x8 l, Y0 l( F │ │ 01_Course_Introduction_Part_1__9-53.txt3 g. L8 ?6 q5 K9 I2 T
│ │ 02_Course_Introduction_Part_2__8-49.mp4
1 U6 h6 `3 l& c/ {' g: ]% Z+ g/ y) @5 q │ │ 02_Course_Introduction_Part_2__8-49.srt
" W0 F; q7 S" H, p2 n9 J( P │ │ 02_Course_Introduction_Part_2__8-49.txt
/ Q9 P& b% d: D' J7 W& | │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.mp4
' G8 P, a! @) M+ j │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.pdf& g& p7 F) V, i# ~
│ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.srt
& U1 u, I. Q. P) \. N. C6 B) M │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.txt$ L" @* }5 t1 s, Q' g. o2 [: Y
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.mp45 O/ W% ?% o* c8 L0 K3 H
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.pdf! m9 p( K0 B5 x+ _% l
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.srt& L' V3 b( _7 ^0 }
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.txt
2 {( Y8 W9 J4 ~6 d3 m8 e3 \ ? │ │ 05_The_Field-_Playing_with_C_15-19.mp4
0 C. [4 w! V. D3 `+ x │ │ 05_The_Field-_Playing_with_C_15-19.pdf) \. F- u* x$ N( R
│ │ 05_The_Field-_Playing_with_C_15-19.srt4 g: P( q7 _' D$ @8 i" V
│ │ 05_The_Field-_Playing_with_C_15-19.txt
w8 s, y, k9 T; R& A4 a0 A+ R0 ~ │ │ 06_The_Field-_Playing_with_GF2__10-28.mp4
) F# v5 ~3 J( }4 H: H: i2 Z( b6 | │ │ 06_The_Field-_Playing_with_GF2__10-28.pdf6 a9 u# n, x3 Q! U# ^6 O, w1 ~
│ │ 06_The_Field-_Playing_with_GF2__10-28.srt
& \: [$ U4 W0 j7 b. U/ v0 E$ b │ │ 06_The_Field-_Playing_with_GF2__10-28.txt8 q1 h( S, a* u5 S9 |
│ │ 4 B" f# x3 O% Z; z I6 B2 q
│ ├─02_Week_1-_The_Vector2 [$ f+ S& y& d, r( l: A% C
│ │ 01_The_Vector-_What_is_a_vector__8-20.mp4
$ Z3 \: B" Q+ R │ │ 01_The_Vector-_What_is_a_vector__8-20.pdf
) h& T. j! o& } y4 y │ │ 01_The_Vector-_What_is_a_vector__8-20.srt7 K# X- M& j: b# n K5 q1 F
│ │ 01_The_Vector-_What_is_a_vector__8-20.txt: s/ \5 S0 i- } ~
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.mp44 w- p4 \& b1 q0 Y9 j
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.pdf3 B2 ?# c* s, s3 ?) x* S
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.srt
8 ?& H5 v& O+ M │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.txt
$ p" }0 ?, ~( ?/ _9 g l │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.mp4% z6 j% L7 o5 [7 X' F" [
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.pdf
2 H$ d& O; u: Z4 ]7 m; D) }2 g │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.srt
. Z6 S* D% }1 }6 K& q2 E │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.txt' o$ ^! r" g' N" \: v4 c- V
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.mp4
0 C: U* {( S) i. g/ `5 i% E │ │ 04_The_Vector-_Vectors_over_GF2__9-18.pdf
0 t9 D2 j! U' f# S) J) O │ │ 04_The_Vector-_Vectors_over_GF2__9-18.srt
! B/ j) M' Q: d6 } │ │ 04_The_Vector-_Vectors_over_GF2__9-18.txt, [( x- E1 B2 W
│ │ 05_The_Vector-_Dot-product__8-49.mp4. U( Q0 L& R$ N, x4 m3 l
│ │ 05_The_Vector-_Dot-product__8-49.pdf) \' w* @ }; W& M" z/ ?/ F- z- j! G
│ │ 05_The_Vector-_Dot-product__8-49.srt$ x) Z/ [0 V2 I
│ │ 05_The_Vector-_Dot-product__8-49.txt& H* v$ p* V$ |7 {
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.mp4, X4 i b: j7 v3 ?- U3 x
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.pdf
Q' J" C0 Z' V% N2 P. X4 {4 U! L │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.srt
7 f1 G; _' n& u2 h1 m) r1 u) {: X; { │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.txt
2 Q1 V7 t0 E9 I$ ` │ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.mp4+ E+ ^! P- _" ?2 B$ U# [2 G
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.pdf
- ~) b( d( E+ x: e* Z% I7 K/ _5 u( { │ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.srt
! o$ O, Y9 L% v1 \, I │ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.txt w! _4 G: P' p
│ │
6 Z; T& C1 O$ g │ ├─03_Week_2-_The_Vector_Space
; T% F& C/ H5 N( V) h │ │ 01_The_Vector_Space-_Linear_combinations.mp4
4 ]+ v$ y. c0 L p' B │ │ 01_The_Vector_Space-_Linear_combinations.pdf# g& }4 N( m/ w
│ │ 01_The_Vector_Space-_Linear_combinations.srt
# c/ y* p5 K% g. p. \, |9 v; S& ] │ │ 01_The_Vector_Space-_Linear_combinations.txt4 _! f5 l' w1 T
│ │ 02_The_Vector_Space-_Span.mp4
3 c$ _& u( R. S( w# p7 k' _; n I │ │ 02_The_Vector_Space-_Span.pdf
* B: n0 {1 J. M7 s; n │ │ 02_The_Vector_Space-_Span.srt) M7 I3 F9 j; O) j8 E
│ │ 02_The_Vector_Space-_Span.txt
) D6 @/ w, U( @( _9 Z7 ?$ J │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.mp41 l. n0 e* J7 z7 k
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.pdf" ]# ?/ {; D3 |" q' W7 _
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.srt
* w. c4 W! r! L4 Y$ u │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.txt8 K2 q7 y- `! i: G' G/ U
│ │ 04_The_Vector_Space-_Vector_spaces.mp4% A( V2 P9 ~% f( j2 P
│ │ 04_The_Vector_Space-_Vector_spaces.pdf) k9 g- r5 _8 L7 |5 _3 I$ E' l
│ │ 04_The_Vector_Space-_Vector_spaces.srt
; M8 Y) D. b; ~7 ~! v9 Q# Z4 l8 B# h │ │ 04_The_Vector_Space-_Vector_spaces.txt& U- _" j+ w! W# K
│ │ 05_The_Vector_Space-_Checksum_function.mp4
3 p: J- L3 `2 \- L$ e │ │ 05_The_Vector_Space-_Checksum_function.pdf
1 O }$ V3 |; V, Y" S │ │ 05_The_Vector_Space-_Checksum_function.srt
1 @2 Y4 w9 p% \7 A( d7 d- m/ w │ │ 05_The_Vector_Space-_Checksum_function.txt
# B+ a- F) K$ n+ b │ │ 5 \1 M4 |! F3 f" u- j# k
│ ├─04_Week_3-_The_Matrix `6 s0 p2 j+ E
│ │ 01_The_Matrix-_What_is_a_matrix.mp48 N2 f8 U7 d" L3 T6 R
│ │ 01_The_Matrix-_What_is_a_matrix.pdf8 u& E7 s4 a5 q D3 E1 m
│ │ 01_The_Matrix-_What_is_a_matrix.srt
) x+ C2 u2 [' P. j │ │ 01_The_Matrix-_What_is_a_matrix.txt
( k1 c* K' M7 _* A6 k2 {# D │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.mp4! `' @; v9 \# [0 r$ \9 O
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.pdf
6 f" ^4 w# i8 Z# ^4 F& V# W │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.srt
# A- h, x5 u' {3 ` │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.txt
/ Y- o' @6 C6 Q. d6 e, K2 U │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.mp4 h4 E* b$ R6 r3 H) t% m7 U0 [+ ^
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.pdf/ s' l b. I) B! y1 d+ \& u/ G
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.srt; Z E) f5 l' d: n$ P
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.txt
8 |3 @3 Z0 L. S% r │ │ 04_The_Matrix-_Null_space.mp4
! n! ^" B) G. A' C │ │ 04_The_Matrix-_Null_space.pdf
6 m1 u, H! W4 }; p; n │ │ 04_The_Matrix-_Null_space.srt: J, ^2 }1 x( i# w+ }4 V8 @
│ │ 04_The_Matrix-_Null_space.txt
$ `1 M+ q3 P5 C$ h I* `2 u, V │ │ 05_The_Matrix-_Error-correcting_codes.mp4
8 ^0 F1 m5 L" A2 q( r │ │ 05_The_Matrix-_Error-correcting_codes.pdf! i& b" ~' d; \+ K/ B# @
│ │ 05_The_Matrix-_Error-correcting_codes.srt
" M/ F8 o* m+ i7 k7 _. m; v │ │ 05_The_Matrix-_Error-correcting_codes.txt
. ?( I5 z& Q8 L │ │ 06_The_Matrix-_Matrices_and_their_functions.mp4- E1 }) P; M2 J+ h) m1 e* u# x( u# X
│ │ 06_The_Matrix-_Matrices_and_their_functions.pdf/ ^3 }6 @# e7 e
│ │ 06_The_Matrix-_Matrices_and_their_functions.srt& V- z8 c6 O: Y3 E3 O5 b7 N
│ │ 06_The_Matrix-_Matrices_and_their_functions.txt
! d! o ]2 |9 M4 N/ |5 S │ │ 07_The_Matrix-_Linear_functions.mp4
8 v* }! i, A( F _ │ │ 07_The_Matrix-_Linear_functions.pdf. ]( S7 u( J9 b9 g& p# H/ Q. v
│ │ 07_The_Matrix-_Linear_functions.srt
1 K) e( z: M7 x) ?1 E( k7 b │ │ 07_The_Matrix-_Linear_functions.txt3 {# q$ K8 o: r5 k, h; b3 ?5 I) t
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.mp4
! w" W' Z' h# M8 m9 W* q- ] │ │ 08_The_Matrix-_Matrix-matrix_multiplication.pdf
1 Z! O( I0 }4 u& K9 ^ │ │ 08_The_Matrix-_Matrix-matrix_multiplication.srt% h+ z* H: Z+ K+ M
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.txt
3 f5 w" i; c) u7 ?) I1 _ │ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.mp47 f- @ ?: z/ R1 P l0 V
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.pdf! P# U/ S; x; A/ F d
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.srt+ i9 a7 N( ^ l# N5 c9 \( v
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.txt
: `, R8 ~" ~( {- J+ |+ m │ │ 10_The_Matrix-_Matrix_inverse.mp4# h: u: }0 I* G! n1 e
│ │ 10_The_Matrix-_Matrix_inverse.pdf
. {; J# N" t1 Q \$ u3 E; q8 N │ │ 10_The_Matrix-_Matrix_inverse.srt
5 ]# `7 }# @4 L4 H( O" h5 p( V │ │ 10_The_Matrix-_Matrix_inverse.txt
: F; }3 t8 }/ V4 @8 `: b1 d& H │ │
8 J( d; A- ^/ W) q. J$ U │ ├─05_Week_4-_The_Basis% B5 ]' }7 Y. E; E. w4 {
│ │ 01_The_Basis-_Coordinate_systems.mp4
! z3 f% |5 x; E5 P/ X1 q4 B │ │ 01_The_Basis-_Coordinate_systems.pdf N9 J1 |$ C& g7 b+ C T& p8 l
│ │ 01_The_Basis-_Coordinate_systems.srt
: n; \% I, H# p) k2 Q4 V │ │ 01_The_Basis-_Coordinate_systems.txt1 \: R0 I: _' |+ \) V5 A I
│ │ 02_The_Basis-_Lossy_compression.mp4
5 c1 N! ^' P) E! T7 f │ │ 02_The_Basis-_Lossy_compression.pdf
$ ?. K; d7 Z* l) Q+ s │ │ 02_The_Basis-_Lossy_compression.srt) m# d! r% p7 k9 D4 C- F5 b! a' T
│ │ 02_The_Basis-_Lossy_compression.txt
# |# y' e' O O! O2 X. I5 T │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.mp4
: _% |7 h. ^7 C! k. |, R/ p │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.pdf0 B7 t+ g, m5 ^& L8 s
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.srt: g9 F2 k6 U& F: Z) P7 o* ?( R: u
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.txt8 D1 K" l, {# J) x2 E
│ │ 04_The_Basis-_Minimum_spanning_forest.mp4
" H+ v- ]) A- e' m0 m1 o2 E" F; F │ │ 04_The_Basis-_Minimum_spanning_forest.pdf
- X% M4 |+ v) J0 @2 M │ │ 04_The_Basis-_Minimum_spanning_forest.srt
3 V7 H+ C6 g% A- G8 B │ │ 04_The_Basis-_Minimum_spanning_forest.txt9 ?0 t$ x7 N; J B* j: f2 M
│ │ 05_The_Basis-_Linear_dependence.mp4
. u% B4 E. Z: R' Y7 D; q │ │ 05_The_Basis-_Linear_dependence.pdf2 [) @# _6 I: U; d4 Y
│ │ 05_The_Basis-_Linear_dependence.srt& x( {. }8 K3 [0 z3 d& H* v7 l
│ │ 05_The_Basis-_Linear_dependence.txt
: r: R# A9 Y+ z1 l: @ │ │ 06_The_Basis-_Basis.mp4; B, y, N& @, e% G. b
│ │ 06_The_Basis-_Basis.pdf$ v0 O& f8 K6 y: [
│ │ 06_The_Basis-_Basis.srt
/ D: V6 I( [% a) g- |! I$ T │ │ 06_The_Basis-_Basis.txt
* ? R- X6 p2 p: x& o6 C │ │ 07_The_Basis-_Unique_representation.mp4( W# j. U7 G9 w
│ │ 07_The_Basis-_Unique_representation.pdf
1 c6 V; i9 c; Q' K; D3 F5 i │ │ 07_The_Basis-_Unique_representation.srt" j8 ]/ H/ ]$ d& T" w
│ │ 07_The_Basis-_Unique_representation.txt
( S7 X- H- j9 q │ │ 08_The_Basis-_Change_of_basis.mp4- ]/ Q" ^& t2 B: m9 a3 |0 t% l
│ │ 08_The_Basis-_Change_of_basis.pdf
0 z5 J }# B! N5 R! O9 ~9 l" n │ │ 08_The_Basis-_Change_of_basis.srt
M/ E( [; n6 K9 e6 y K+ a* R* } │ │ 08_The_Basis-_Change_of_basis.txt
) O4 C# H" [" y0 l │ │ 09_The_Basis-_Perspective_rendering.mp42 ]- Q# Z' n# f7 E
│ │ 09_The_Basis-_Perspective_rendering.pdf
6 F x2 a% u; k+ F% L2 h4 M │ │ 09_The_Basis-_Perspective_rendering.srt$ ?7 o+ m( K7 z( `2 E
│ │ 09_The_Basis-_Perspective_rendering.txt
4 Z& k3 F; F3 b3 D │ │ 10_The_Basis-_Perspective_rectification.mp4
' Z9 ]# T( k/ e6 {- y* t# X- F │ │ 10_The_Basis-_Perspective_rectification.pdf* X4 U3 R( H: {- w2 X9 K
│ │ 10_The_Basis-_Perspective_rectification.srt6 O, `" G' J. n9 b4 L
│ │ 10_The_Basis-_Perspective_rectification.txt) k. S W$ E, n" ]7 G' T
│ │ 11_The_Basis-_The_Exchange_Lemma.mp4( b: G& v, U3 C( t+ w$ S
│ │ 11_The_Basis-_The_Exchange_Lemma.pdf
% l- G& `: o# Z$ W& W8 o, { │ │ 11_The_Basis-_The_Exchange_Lemma.srt# @1 T5 z* m0 H3 d5 j s
│ │ 11_The_Basis-_The_Exchange_Lemma.txt9 P6 Q/ e# r# W- S0 S- A
│ │ * A- M& g' J4 k' F/ J9 F
│ ├─06_Week_5-_Dimension% ^% a. D. q( z: z7 `( M$ \
│ │ 01_Dimension-_The_size_of_a_basis.mp4/ X! n1 g; {5 w% d- A+ G
│ │ 01_Dimension-_The_size_of_a_basis.pdf
7 U. L! ?7 A8 v │ │ 01_Dimension-_The_size_of_a_basis.srt
% K: [6 I/ p7 L/ t1 f* g │ │ 01_Dimension-_The_size_of_a_basis.txt1 J6 D0 M3 ~+ P; S& Q1 X
│ │ 02_Dimension-_Dimension_and_rank_I.mp4; K# F# v9 v, R* q+ E
│ │ 02_Dimension-_Dimension_and_rank_I.pdf- K- B% q5 i7 z2 l! t1 w( t0 F
│ │ 02_Dimension-_Dimension_and_rank_I.srt
$ X( ~' t& K1 y5 ^4 [% @# ]9 @ │ │ 02_Dimension-_Dimension_and_rank_I.txt: Q4 u/ i0 `4 s. K0 R( t
│ │ 03_Dimension-_Dimension_and_rank_II.mp4
% @6 E+ I3 B/ t& c │ │ 03_Dimension-_Dimension_and_rank_II.pdf0 P" l N3 K ?. Y: u
│ │ 03_Dimension-_Dimension_and_rank_II.srt
) T+ K0 Y3 n( U, c │ │ 03_Dimension-_Dimension_and_rank_II.txt
, g4 R* z: E1 L( B+ W- B │ │ 04_Dimension-_Direct_sum.mp4( V- P, p0 N$ g: P& A
│ │ 04_Dimension-_Direct_sum.pdf: e+ f1 @" w# p
│ │ 04_Dimension-_Direct_sum.srt
# j! k1 d1 n/ |( S" d* g │ │ 04_Dimension-_Direct_sum.txt
& Q$ U- `: \* l5 m+ i; U- R- i │ │ 05_Dimension-_Dimension_and_linear_functions_I.mp4) h3 W: L/ V( n6 `% ~9 u. d, ~
│ │ 05_Dimension-_Dimension_and_linear_functions_I.pdf! l4 [ k5 p! F* N$ k. L3 N
│ │ 05_Dimension-_Dimension_and_linear_functions_I.srt
2 t7 Q3 q9 Y+ @4 Z2 b │ │ 05_Dimension-_Dimension_and_linear_functions_I.txt
% ^# y3 k l4 V" G* Q& q# r │ │ 06_Dimension-_Dimension_and_linear_functions_II.mp4
8 H( _7 _% M$ J9 X4 |" U │ │ 06_Dimension-_Dimension_and_linear_functions_II.pdf
" o6 q8 i5 ]. i. I b$ h/ M │ │ 06_Dimension-_Dimension_and_linear_functions_II.srt( P7 L/ A1 l# o% _& t% }- z8 l) f
│ │ 06_Dimension-_Dimension_and_linear_functions_II.txt
! z4 ]. r1 o( r. e$ _0 x! Y( a- f │ │ 07_Dimension-_Two_representations_of_vector_spaces.mp45 [& F. `. u. |. K& o3 p
│ │ 07_Dimension-_Two_representations_of_vector_spaces.pdf. n2 ~% l% P) z+ \5 n/ o
│ │ 07_Dimension-_Two_representations_of_vector_spaces.srt
* v3 {# s" e( z }* C. X1 r' w │ │ 07_Dimension-_Two_representations_of_vector_spaces.txt( `1 U5 k2 W* \* N* J, o3 k
│ │ 08_Dimension-_Threshold_secret_sharing.mp4
0 Q1 _ r# V* H9 ]+ N) u1 n" F ` │ │ 08_Dimension-_Threshold_secret_sharing.pdf C* }6 B2 F+ t, o
│ │ 08_Dimension-_Threshold_secret_sharing.srt4 E# j, n, q) L
│ │ 08_Dimension-_Threshold_secret_sharing.txt4 M0 D+ }7 [& h1 E$ g" @
│ │
; i) Q3 t5 X/ L( z7 } l │ ├─07_Week_6-_Gaussian_Elimination_and_the_Inner_Product2 y. E9 A7 T/ i. Y5 \" M2 w
│ │ 01_Gaussian_Elimination-_Echelon_form.mp4
- O1 Z1 ^7 _' {* u& ^; ?- [ │ │ 01_Gaussian_Elimination-_Echelon_form.pdf
9 m- y! [! Z. b7 f; ?+ z$ }2 s │ │ 01_Gaussian_Elimination-_Echelon_form.srt
1 z, k8 }, V x/ N9 ? │ │ 01_Gaussian_Elimination-_Echelon_form.txt! k# W$ d7 K/ s* T; e
│ │ 04_Gaussian_Elimination-_Factoring_integers.mp4
/ c- ]/ I) |. P7 O$ t, Y8 v( x, [ │ │ 04_Gaussian_Elimination-_Factoring_integers.pdf( t5 v0 l) H( C. `
│ │ 04_Gaussian_Elimination-_Factoring_integers.srt9 w1 P u% e% V, h$ n L+ \0 J
│ │ 04_Gaussian_Elimination-_Factoring_integers.txt) l, X6 h5 y" K) B
│ │ 05_The_Inner_Product-_The_inner_product.mp4* I) _6 h. t" b% \8 t
│ │ 05_The_Inner_Product-_The_inner_product.pdf
: G! P+ B6 K' Q! |, j$ v4 V │ │ 05_The_Inner_Product-_The_inner_product.srt
- R$ V' V/ h& y) S! q │ │ 05_The_Inner_Product-_The_inner_product.txt5 D$ A( v% i. A0 B4 g D
│ │ 06_The_Inner_Product-_Orthogonality.mp4' t, j6 O8 @" G0 I1 R# ^# a
│ │ 06_The_Inner_Product-_Orthogonality.pdf4 ?( p& c ~# d: V; s; n0 b7 l$ `
│ │ 06_The_Inner_Product-_Orthogonality.srt% J) D2 m. ^. b, _
│ │ 06_The_Inner_Product-_Orthogonality.txt
8 }7 r1 S, } d- T9 d │ │ 5 d" e8 b9 D& z" d+ x) H2 t
│ ├─08_Week_7-_Orthogonalization
6 \6 l% Q$ N& ~4 m* ^ │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.mp4
/ l5 C7 [$ {. s, I3 U3 q) {% p │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.pdf
4 z5 h( }7 r [, D+ t! G0 b │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.srt
. _& o7 p) f. ?- t% j' I& { │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.txt% F Q' p3 w9 O4 s: _+ D _+ x
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.mp4
# G# m: U4 }) v │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.pdf
! h9 N& w. ^: V: f4 Q │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.srt
/ O e6 q! f7 g5 T: } │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.txt
9 k. W9 ~7 K" z+ P: p: ] │ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.mp48 n: r% d- d% ^* r$ ]1 N0 h
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.pdf
9 b8 f) W$ w' o3 u( q │ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.srt0 i8 L. z& |. V
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.txt3 l. n) J0 V* H6 `5 Y' y
│ │ 04_Orthogonalization-_Computing_a_basis.mp4' r. A5 |" \4 A& T7 n" z
│ │ 04_Orthogonalization-_Computing_a_basis.pdf
' l% a: ]! p) B% B+ \) P │ │ 04_Orthogonalization-_Computing_a_basis.srt
, F. G4 x7 H- m+ k2 H' @' x$ y │ │ 04_Orthogonalization-_Computing_a_basis.txt$ U5 B6 s) E) u' K P8 Y
│ │ 05_Orthogonalization-_Orthogonal_complement.mp4
. a. W; Q0 v4 Y( H- `1 r3 B │ │ 05_Orthogonalization-_Orthogonal_complement.pdf
* V* V# `" s4 i# Q% ?; i. I9 a │ │ 05_Orthogonalization-_Orthogonal_complement.srt
$ L: ~* h2 v+ a' {1 { │ │ 05_Orthogonalization-_Orthogonal_complement.txt: E# t/ u% @) ^; b7 Z) C
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.mp4
; N; H) u: f0 p/ O! H │ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.pdf
# l+ a* ~( C! T) S2 x │ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.srt- L$ _9 N3 V' @1 D% @! D+ s
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.txt
2 b; ^' ~8 C6 M: G% r │ │ 07_Orthogonalization-_The_QR_factorization.mp4
& `' i& V- Z2 z+ Q) M) V │ │ 07_Orthogonalization-_The_QR_factorization.pdf* o# L+ O5 p P" `8 A3 l1 O
│ │ 07_Orthogonalization-_The_QR_factorization.srt
$ {) E% F D {$ G% V6 { │ │ 07_Orthogonalization-_The_QR_factorization.txt
. K2 P. p5 ?3 ~7 k6 G2 l │ │ 09_Orthogonalization-_Applications_of_least_squares.mp4
/ U T. m5 e' y }1 ?% r" W. O │ │ 09_Orthogonalization-_Applications_of_least_squares.pdf
b* L# M3 Y' a i, d$ l6 a1 h8 y │ │ 09_Orthogonalization-_Applications_of_least_squares.srt
3 E( u; q! _1 v, x6 | │ │ 09_Orthogonalization-_Applications_of_least_squares.txt' J$ U( o6 x. t) n# G) h0 q/ M5 A/ H
│ │
$ x, F$ s; ]. S8 Z/ b$ V# q │ └─09_Tutorials
8 |& `6 p' p. o( g& r/ u/ e │ 01_How_to_submit_assignments.mp4
. {" I1 P: ~! l4 T │ 01_How_to_submit_assignments.srt
' W( N3 W$ I$ }: U( X% y │ 01_How_to_submit_assignments.txt1 _* x) \/ U O; l. z! q
│ 3 Y4 r# ^; n. A- x
├─Big Data, Cloud Computing, & CDN Emerging Technologies
* _) U) V% {3 c │ └─Big Data, Cloud Computing, & CDN Emerging Technologies
: T2 ~2 B2 o& R │ │ 下载说明.txt
7 G: t) I3 t5 F4 {1 O │ │ 关注我们.png
3 t& \ J2 a; q* f+ l7 @& { │ │ 攻城狮论坛=网络技术+编程视频.url
5 b; D. r1 Z& Z9 O, L │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt C d" Y4 X* g* y9 Q6 O4 p5 U
│ │
6 j3 a: T' ^0 ~# q │ ├─01_cloud-computing
$ g: Q2 Y9 }0 @! G" ^ │ │ └─01_cloud-computing
" I& ?: S5 f& I │ ├─02_big-data
; M# p0 M8 V/ D' v' {% R │ │ └─01_big-data
' v Q9 U' o7 W& t6 t9 R2 N │ │ 01_big-data-examples.mp4
\1 D4 v4 \( }# E │ │ 01_big-data-examples.srt7 R) h$ `' H1 A; E( _ U3 S
│ │ 02_big-datas-4-vs.mp4
+ _1 e7 o2 Q7 m- R% M$ I3 n │ │ 02_big-datas-4-vs.srt
2 h! ?4 _0 Q' G' Q/ U8 D O5 @ │ │ 03_hadoop.mp4
7 D& q7 l- L4 @ │ │ 03_hadoop.srt
2 S) a& [6 T) f9 t/ Q │ │ 04_mapreduce-vs-rdbms.mp46 T) W8 I8 B3 r' r
│ │ 04_mapreduce-vs-rdbms.srt0 Q9 k$ S* j& ?
│ │ 05_mapreduce.mp4
. |4 v$ q8 g. Q6 z8 I │ │ 05_mapreduce.srt0 Q. d0 D2 Y" M! p
│ │ 06_hdfs.mp4( N7 W& D9 I5 f+ m; h6 U
│ │ 06_hdfs.srt3 C; W& m* R8 ?' @$ Q
│ │ * w3 s# N3 v/ [3 |9 G
│ └─03_cdn-content-delivery-network
n6 L, ]7 H9 u% J' A" f. ` ├─Duke Image and video processing From Mars to Hollywood with a stop at the hospital o* f* v1 w; O9 }. v$ [- _% N
├─Interactive Computer Graphics$ B0 h7 i, I0 u/ e
│ └─Interactive Computer Graphics* n. ^& j8 }1 T$ O
│ interactivegraphics-Torrent-Week1_All.zip.torrent0 q% Y, y6 ?9 w- T, U
│ interactivegraphics-Torrent-Week2_All.zip.torrent( Q8 q6 U" h4 g0 E
│ interactivegraphics-Torrent-Week3_All.zip.torrent
7 B' \' m" q7 d3 f* P$ R8 k: v( F │ interactivegraphics-Torrent-Week4_All.zip.torrent
5 b) U% V* v- ? │ interactivegraphics-Torrent-Week5_All.zip.torrent
+ x# S1 v$ X! f │ interactivegraphics-Torrent-Week6_All.zip.torrent! k7 i1 i+ M3 y" `' U: [+ w0 K
│ interactivegraphics_Torrent_Week1_All.zip, k* @8 J! w! ]4 b: t- m# g' O9 r
│ interactivegraphics_Torrent_Week2_All.zip2 \7 D- g, ^ u% J& e$ V
│ interactivegraphics_Torrent_Week3_All.zip0 S4 ^% U3 N+ s! n+ m/ t' s2 f
│ interactivegraphics_Torrent_Week4_All.zip$ e9 o+ H* \- |- W
│ interactivegraphics_Torrent_Week5_All.zip
1 x5 f5 p, ^) r" u. ` │ 下载说明.txt
( P, |" R5 g+ m" D │ 关注我们.png
: ?# G; G- F" {3 T, S/ W" Z │ 攻城狮论坛=网络技术+编程视频.url d4 b1 O; G2 A
│ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt6 x/ Y n8 i5 y3 F% `
│
0 f- B$ V4 z4 x) D# S- X! s ├─Malik - Computer Vision
1 b. Z! P2 f1 \& }4 G │ └─Malik - Computer Vision
( K0 S: h0 d T# y* @- z; G E6 s │ 001_Overview.mp4
6 x. v4 R0 |- V* B7 j7 b" E │ 001_Overview.srt
+ \$ b1 v2 v5 |' q+ N5 [$ y+ t │ 002_Fundamentals of image formation.mp4- j3 T: P7 v5 z7 Y+ S, O
│ 002_Fundamentals of image formation.srt
# K6 [0 j% g0 I/ A- d │ 003_Fundamentals of image formation - part II.mp4 i, G7 N- ?$ ^
│ 003_Fundamentals of image formation - part II.srt! t0 y8 W( N0 \* }$ ~
│ 004_Rigid body motion.mp44 s9 G, t5 l: ~9 z, t
│ 004_Rigid body motion.srt y6 Z7 U- A& ?8 T. d* N: f- k$ l$ }
│ 005_Orthogonal transformations.mp4
& }; p5 j' |( b │ 005_Orthogonal transformations.srt
4 [; b6 l' A( l$ R │ 006_Orthogonal transformations - Orthogonal Matrices.mp4
8 D5 E+ h. |" \. p c" C │ 006_Orthogonal transformations - Orthogonal Matrices.srt
/ g0 ^0 O1 M' u │ 007_Orthogonal matrices - Rotations and reflections.mp4% y# f- s; O* N/ F% Z$ s. y5 x
│ 007_Orthogonal matrices - Rotations and reflections.srt% h4 ~$ ?3 k+ t g% Q" l
│ 008_Parametrizing Rotations in 3D.mp4
8 Z) f# F7 c1 Z6 Z) [ │ 008_Parametrizing Rotations in 3D.srt
8 w/ y" C' ? { │ 009_Euclidean, Affine and Projective Transformations.mp4$ I( R7 o4 T' h7 Q; k2 F
│ 009_Euclidean, Affine and Projective Transformations.srt( }- L9 p: _5 U3 V
│ 010_Dynamic Perspective - I [15 mins].mp49 G3 A* S$ ~4 v: k/ |& G+ v
│ 010_Dynamic Perspective - I [15 mins].srt; v8 |' K% c/ a) b; e/ ]
│ 011_Dynamic Perspective - II [25 mins].mp4/ }/ k& ]$ z6 h( \% D2 E8 f/ f
│ 011_Dynamic Perspective - II [25 mins].srt
6 C% T; a' p& k6 G$ u/ q9 _ │ 012_Binocular Stereo I [23 mins].mp4
9 h: G' d( `* R7 T │ 012_Binocular Stereo I [23 mins].srt# G8 B7 }" {/ ?( ?: h
│ 013_Binocular Stereo II [17 mins].mp4
8 e4 m: Z @2 F }0 ^ Y │ 013_Binocular Stereo II [17 mins].srt
4 A( F. ?) r6 g0 S1 G │ 014_Binocular Stereo III [13 mins].mp4
: [; I% g/ g( g │ 014_Binocular Stereo III [13 mins].srt( z+ z9 }6 O4 h9 g( B
│ 015_Binocular Stereo IV - The Essential Matrix [26 min].mp49 g) U& J( d: W$ }9 C1 J) D0 K
│ 015_Binocular Stereo IV - The Essential Matrix [26 min].srt8 A. ^# |2 a% A j a
│ 016_Radiometry.mp4
/ S; _! t2 S" f/ b% q# ~ │ 016_Radiometry.srt- k2 k/ C1 R# K+ I' i
│ 017_Image processing.mp4
( A8 `7 _+ Y4 g {6 ?5 H5 l │ 017_Image processing.srt
2 b( r' U6 N/ c3 a. [# v │ 018_Image Processing2.mp4* t5 A# i+ p3 x* f; F& ]
│ 018_Image Processing2.srt7 Y4 L1 j9 ], D- d
│ 019_Orientation histograms [7 min].mp4
- H( e* g& u" Z- X5 y% `: } │ 019_Orientation histograms [7 min].srt
( M) m/ J3 i2 ~) I; q │ 020_Handwritten digit recognition - Introduction.mp4
7 d2 \ ^. I1 Z- B# a │ 020_Handwritten digit recognition - Introduction.srt3 h* u' l% G$ o1 i! g
│ 021_Support Vector Machines.mp4
& J9 H/ q+ X# ]3 z8 S │ 021_Support Vector Machines.srt
8 w3 \1 `4 p" X" n; I& a- t │ 022_Transformation Invariance and Histograms.mp4) X& H% E' N7 \, |) C! J; g v; G1 o
│ 022_Transformation Invariance and Histograms.srt$ r6 m0 J3 @3 C6 U0 S
│ 023_Digit recognition using SVMs.mp4
# \( y/ b3 c# }3 a( J │ 023_Digit recognition using SVMs.srt
4 y p# x" n& ]* s2 V& y6 q: Z( ? │ 024_Random forests.mp4
' G& R# u6 N8 N. S4 t" m │ 024_Random forests.srt
* N6 ]) L0 E8 I }& F% i) V; K( e4 U& | │ 025_Detection of 3D objects.mp4" P3 A0 k1 G& r0 \6 M ~
│ 025_Detection of 3D objects.srt
* u. O; Y5 g( i$ T( e │ 026_Concluding Remarks.mp4
! W1 I% Q2 q# @. e b# @ │ 026_Concluding Remarks.srt4 G, ~' X! Z' n! S0 D0 ?2 w, @
│ 下载说明.txt6 ]( S/ a4 m) C: K
│ 关注我们.png6 s# P2 Z! u% m1 m7 d, t
│ 攻城狮论坛=网络技术+编程视频.url% Y* }* K6 }3 y
│ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
, W! E/ Z9 C2 N. Y/ L3 f: F │
& ^- m6 a$ k( j3 U& q H ├─Practical Machine Learning
) e) V* A5 _% W# ~2 {& V" @ |4 F │ └─Practical Machine Learning( g2 k6 n* E. H% B _% ]& N2 X
│ │ 下载说明.txt4 J- X) T2 R9 r1 t7 E7 }
│ │ 关注我们.png# l0 S/ j8 W8 t( c7 s1 ?
│ │ 攻城狮论坛=网络技术+编程视频.url
% X: q# I$ u2 | │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt$ q$ y3 _1 w4 ]' F% a
│ │
# a0 ^3 M9 m, `! }9 z& M4 K8 n; e │ ├─Week1
8 T7 A# {& v: N; k │ │ 001predictionMotivation.pdf8 g1 [3 ?# s4 m, |
│ │ 002whatIsPrediction.pdf
. I: _. ]: A' V5 a0 S" J6 d │ │ 003relativeImportance.pdf
8 g: A- x# I* D7 U; Y/ s4 [+ W! z │ │ 004inOutSampleErrors.pdf# [; [1 O+ d+ u2 ]5 Y
│ │ 005predictionStudyDesign.pdf/ u) N# W/ _. ]6 I
│ │ 006typesOfErrors.pdf
" J. q8 C$ F8 U │ │ 007receiverOperatingCharacteristic.pdf
$ K' Y5 ?5 ~' e" T( O9 F │ │ 008crossValidation.pdf
4 e4 \9 ^2 ^0 }8 M X7 } │ │ 009whatData.pdf- H3 ^2 V" g3 K
│ │ 1 - 1 - Prediction motivation (8-26).mp4- S' b6 |3 b+ q/ n% A+ s
│ │ 1 - 1 - Prediction motivation (8-26).srt
( l+ m: ~7 b! |% g" M0 B │ │ 1 - 2 - What is prediction- (8-39).mp4
9 ]( [+ p: g4 i% d5 w0 C" ^ │ │ 1 - 2 - What is prediction- (8-39).srt$ o( m y1 Y3 E& F8 }5 A, [7 t, [
│ │ 1 - 3 - Relative importance of steps (9-45).mp4
/ X4 k* U7 q O │ │ 1 - 3 - Relative importance of steps (9-45).srt
- h' G. v8 s# `/ _% Z+ N% d │ │ 1 - 4 - In and out of sample errors (6-57).mp4! k' `2 [, s% ?* W9 s8 B
│ │ 1 - 4 - In and out of sample errors (6-57).srt
+ N9 A. N2 D, a7 [! L$ _ │ │ 1 - 5 - Prediction study design (9-05).mp4
: T7 r, ]' x% `2 F/ ]' X │ │ 1 - 5 - Prediction study design (9-05).srt
. M% y$ Q: K6 v5 z0 r4 K# z │ │ 1 - 6 - Getting Data Overview (1-34).mp4
5 z9 h1 E! c% Y1 V% V% G │ │ 1 - 6 - Types of errors (10-35).mp4/ X$ f3 ]5 t3 z6 a0 m% f2 G
│ │ 1 - 6 - Types of errors (10-35).srt. u2 w _3 _ R) d( I
│ │ 1 - 7 - Receiver Operating Characteristic (5-03).mp4
8 ?9 G7 f1 \4 N# O, ~* E3 x; \( A │ │ 1 - 7 - Receiver Operating Characteristic (5-03).srt- ^. C8 N$ x" Y( F3 O
│ │ 1 - 8 - Cross validation (8-20).mp4
* L+ U$ c1 u, Z9 D0 M │ │ 1 - 8 - Cross validation (8-20).srt
! R+ q, [! R; z( O6 N │ │ 1 - 9 - What data should you use- (6-01).mp4
' s( v* I6 `9 t) a0 |; X* B │ │ 1 - 9 - What data should you use- (6-01).srt6 [$ T# U; c9 p) [% o
│ │ ~6 M6 i% O" M1 v& S
│ ├─Week21 V8 j! j& N, [: o0 g1 b, l
│ │ 010caretPackage.pdf5 g+ X' t. M$ r: R% I d
│ │ 011dataSlicing.pdf; E& g, b, Q ^9 _8 w( R& L
│ │ 012trainOptions.pdf4 F+ }2 f/ y0 k% _
│ │ 013plottingPredictors.pdf7 d$ F' H9 n% @7 Y# ]
│ │ 014basicPreprocessing.pdf
3 F7 }9 a5 t! b │ │ 015covariateCreation.pdf
0 U: K: M R8 u% A' m# Z$ V │ │ 016preProcessingPCA.pdf
) f. o0 V1 i( f' C& p I; ` │ │ 017predictingWithRegression.pdf
, S% k x' N+ ^$ K) I: c( W7 f │ │ 018predictingWithRegressionMC.pdf" ]; A5 M5 I4 A) {4 m% j
│ │ 2 - 1 - Caret package (6-16).mp4
; b7 _- s3 `5 }$ t* {& t │ │ 2 - 1 - Caret package (6-16).srt
6 y: @' k8 Z) i, O2 O5 Z* V3 B │ │ 2 - 1 - Caret package (6-16).txt
) t# e4 v! h; `7 u9 ~* K │ │ 2 - 2 - Data slicing (5-40).mp4
- P& p# W; `& u9 O$ N │ │ 2 - 2 - Data slicing (5-40).srt. t5 F+ n j8 Z& c; }! p
│ │ 2 - 2 - Data slicing (5-40).txt7 K0 |1 h. A$ }8 `' u7 L
│ │ 2 - 3 - Training options (7-15).mp4
6 l0 C% ~; F5 F# `/ [( B; s4 w │ │ 2 - 3 - Training options (7-15).srt
( [7 c1 {4 n9 H% T │ │ 2 - 3 - Training options (7-15).txt
& d* |6 I+ Q* Q- ]3 O, F! l │ │ 2 - 4 - Plotting predictors (10-39).mp4! b0 u5 x8 l& q5 Y; z2 W* u6 a/ u
│ │ 2 - 4 - Plotting predictors (10-39).srt7 O. L" l+ w& `9 b# d c/ a
│ │ 2 - 4 - Plotting predictors (10-39).txt
$ i+ f1 ]' m2 c' H │ │ 2 - 5 - Basic preprocessing (10-52).mp48 u6 h' Y9 Z, p- B3 M$ p
│ │ 2 - 5 - Basic preprocessing (10-52).srt6 U0 B- S3 }* O; G
│ │ 2 - 5 - Basic preprocessing (10-52).txt
$ H- T" N8 j8 z3 e: ^ │ │ 2 - 6 - Covariate creation (17-31).mp4
- y& |7 D8 u! v( M3 q4 @4 k3 f │ │ 2 - 6 - Covariate creation (17-31).srt1 Q* b! [% n% G0 l% ]/ J
│ │ 2 - 6 - Covariate creation (17-31).txt
4 a( E9 K! V% B) l │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).mp4
+ @& i1 z3 B# T9 O, v$ c │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).srt
! u* X. U, J* l$ T │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).txt
! B3 Q$ T3 r8 A( M" J0 D& V6 F1 b0 { │ │ 2 - 8 - Predicting with Regression (12-22).mp4
. T% {8 V, V0 _( i- O. g- n$ b │ │ 2 - 8 - Predicting with Regression (12-22).srt- _( {& J" S2 {6 V
│ │ 2 - 8 - Predicting with Regression (12-22).txt
9 c( w( f2 B3 u' a j │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).mp48 h% O x+ Z. N# V+ I2 k
│ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).srt
$ }4 D( f6 p) q1 | │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).txt1 y" F- v, o& _7 p
│ │ - r& Q. W* ^( H/ y, n D$ z
│ ├─Week3
7 M8 B9 q0 i, V4 |( l1 S1 h │ │ 019predictingWithTrees.pdf
0 v! A5 ]0 i8 a5 U$ H │ │ 020bagging.pdf# a% b6 P5 |% v
│ │ 021randomForests.pdf4 S4 M2 {3 G5 }: p9 ~1 e9 r" M
│ │ 022boosting.pdf
9 Z4 B& g3 k% g │ │ 023modelBasedPrediction.pdf ` ?0 Z5 m* D5 W& i+ K
│ │ 3 - 1 - Predicting with trees (12-51).mp46 f& q7 T6 _% ]) e
│ │ 3 - 1 - Predicting with trees (12-51).srt
& m( L- ]+ v/ V+ h │ │ 3 - 1 - Predicting with trees (12-51).txt
( r: |& q% M H9 ~& K3 l9 L9 \ │ │ 3 - 2 - Bagging (9-13).mp4% c+ K% Y# w$ j
│ │ 3 - 2 - Bagging (9-13).srt
9 n3 k0 \: p+ [/ W │ │ 3 - 2 - Bagging (9-13).txt
4 }/ L6 d% |8 ~' v" y: | │ │ 3 - 3 - Random Forests (6-49).mp4
; Q: b/ P& W7 c- y3 n │ │ 3 - 3 - Random Forests (6-49).srt% j4 N: O: Q' L. A# O
│ │ 3 - 3 - Random Forests (6-49).txt: N9 A5 H0 b- T" F, d
│ │ 3 - 4 - Boosting (7-08).mp40 a9 E l' p% l8 ]& V( I- r4 L. _
│ │ 3 - 4 - Boosting (7-08).srt
3 b; I5 r+ F9 ^% ?. j │ │ 3 - 4 - Boosting (7-08).txt0 k2 R5 u1 y! E- o4 k
│ │ 3 - 5 - Model Based Prediction (11-39).mp4- Q& @# u/ a+ ]2 ^% G+ f w8 p
│ │ 3 - 5 - Model Based Prediction (11-39).srt
' ^1 S$ E& m0 r! J │ │ 3 - 5 - Model Based Prediction (11-39).txt
: h5 k7 O0 Y4 v& Z$ a A1 M u │ │
' T5 C; `% [# ]. Y( W │ └─Week4
3 `' {+ [1 Q6 x6 k. c2 s │ 024regularizedRegression.pdf
9 W' `. |8 q5 e8 @" _/ S │ 025combiningPredictors.pdf
2 p" }( C/ F: \( X │ 026unsupervisedPrediction.pdf
/ t9 n5 [2 l: C0 E+ k9 P. k* m y │ 027forecasting.pdf
, J+ f! p- ~* l9 s │ 4 - 1 - Regularized regression (13-20).mp4! e: ]5 Q7 X& E6 y: ^
│ 4 - 1 - Regularized regression (13-20).srt
5 j7 y: i5 R6 p- v b+ n" d │ 4 - 1 - Regularized regression (13-20).txt i/ w% V* l& r6 N
│ 4 - 2 - Combining predictors (7-11).mp49 c9 L Q3 e- \0 j" w# p* {
│ 4 - 2 - Combining predictors (7-11).srt9 H0 Y) F& u1 ]9 r6 m
│ 4 - 2 - Combining predictors (7-11).txt# f ~9 q) W$ O8 d7 Q) F
│ 4 - 3 - Forecasting.mp47 n, V- g, B! t1 P" P
│ 4 - 3 - Forecasting.srt
3 i2 ]6 @8 w3 T n) U │ 4 - 3 - Forecasting.txt
0 R; I7 o$ E. H: O1 S │ 4 - 4 - Unsupervised Prediction (4-24).mp4: _6 r/ P* F4 K. W
│ 4 - 4 - Unsupervised Prediction (4-24).srt3 V; J6 p6 ]1 {+ H( R/ M- T: R
│ 4 - 4 - Unsupervised Prediction (4-24).txt+ Y9 D! P, l/ N) U
│ # G- S9 K4 C" S1 B9 p
├─Sapiro - Image and video processing
* ]9 b. ?1 a( m! c6 `) H C! S# X │ 01.pdf- {& M* s7 |2 K2 e
│ 02.pdf6 f; J/ i- t* p0 [* E) D
│ 03.pdf0 X$ `1 ~, ~) H2 H0 ^& |( ^3 V, Z7 {
│ 04.pdf
' X' M/ Q7 @( Z$ S B+ [7 D │ 05.pdf
! t8 Q$ n, p. a6 f │ 06.pdf4 h8 M k9 T% a7 D3 P
│ 07.pdf
9 M! n" v5 U, [ T1 Z) K │ 08.pdf
" p8 A/ L' I0 N9 R T; X │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.mp4
7 w# `) h) U1 F; T/ Y │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.srt
2 U7 p2 k- J: I" `( J' C │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.txt: i, V( ~: H# b) ?$ U8 R
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.mp4. |* ?) A$ U- s+ y6 A
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.srt0 J T! L# v3 Q
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.txt
0 g; |* Y( B8 S& Q │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.mp4: J; }, h/ g& y5 ^6 ?) W
│ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.srt+ Q0 z5 t/ f1 o! v' A4 X
│ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.txt: |% U2 }( C0 d, a6 ^3 K4 V% {
│ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.mp4
" L" [1 ^& _$ T% r4 }4 k% S; p │ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.srt5 j" c' w- O* `" J
│ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.txt) B% K! M6 x/ a7 Q+ ^
│ 10 - 5 - 4 - Thanks.mp41 E! Z$ U* r5 `
│ 10 - 5 - 4 - Thanks.srt. N9 Y0 w6 K F5 U, Q+ n& b$ S
│ 10 - 5 - 4 - Thanks.txt! `4 t% j: v7 N) P* f4 j. X8 B: I
│ 2 - 1 - 0 - Welcome and Start Here.mp4( u- D/ }. g4 p. q' @+ L9 A1 z6 |& @
│ 2 - 1 - 0 - Welcome and Start Here.srt
$ S7 v U% E; \# P, \$ H │ 2 - 1 - 0 - Welcome and Start Here.txt
# ^: b2 u: K: `8 D2 t' n" i │ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.mp4
( R0 z# i) q/ o5 v! Q │ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.srt9 R0 ?- u, i+ Q
│ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.mp4
& R: P% y- R; U7 p) { │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.srt
0 v. v% }' v. i+ o │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.txt
; ?2 Y; A+ l. i6 I5 N0 R │ 2 - 4 - 2 - Course logistics - Duration 0242.mp4
5 v, ~/ r. N* z# ~( b+ L% T/ E. F │ 2 - 4 - 2 - Course logistics - Duration 0242.srt' \( D' @5 R1 J+ i- H
│ 2 - 4 - 2 - Course logistics - Duration 0242.txt) C6 X4 J& i" y" z2 [- u, J/ M6 Z# E* @( z
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.mp48 i- j7 @* G" E+ Q, [( [
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.srt
% w6 ^! Q: }! M/ @; c │ 2 - 5 - 3 - Images are everywhere - Duration 0631.txt6 U8 b5 i% } z, M r& Y: A
│ 2 - 6 - 4 - Human visual system - Duration 1710.mp4
0 c- k8 f/ p ^8 _; } │ 2 - 6 - 4 - Human visual system - Duration 1710.srt
! l% M3 M6 o5 f/ r │ 2 - 6 - 4 - Human visual system - Duration 1710.txt
( H+ R" _, E& U0 S │ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.mp4
) u! e8 H& f3 x% t& B* s: o Q │ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.srt
8 {. l0 G1 R/ M! u │ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.txt$ k1 B+ R6 E7 y6 n& m7 b. M. a
│ 2 - 8 - 6 - Simple image operations - Duration 1705.mp4
" t; ~/ x: S: T5 u' Y; s │ 2 - 8 - 6 - Simple image operations - Duration 1705.srt U3 K* q' b( m% {* q
│ 2 - 8 - 6 - Simple image operations - Duration 1705.txt, l ?3 }2 U# B6 e/ M" u0 p
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.mp4. x4 H$ I# r' Q+ z% U1 D2 S& R
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.srt
9 E8 C- O: i& W. ?: A7 T1 i; ~ │ 3 - 1 - 1 - The why and how of compression - Duration 1416.txt2 d% [2 a8 f# V% D& p( v6 ]
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.mp4: Z7 q; p$ E6 m, ]' t3 _
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.srt+ [; i5 G1 K+ P- l! K
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.txt% Z9 H# R& A( |8 t. T6 J' ]
│ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.mp4
" f2 a* X/ N3 } │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.srt
7 J) y, | @3 R5 a+ G │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.txt8 \( W) s; ^' C& T# A8 ^
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.mp4
$ n& t/ K1 f0 R4 d S0 | │ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.srt1 o3 C* A7 E2 a' J, O2 j4 y m
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.txt
) k- I. u! J8 Q6 f& u │ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.mp4& C2 y: \% g9 R7 Z$ z
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.srt3 g) Q; c' C/ J' H4 S5 J& V1 f
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.txt
8 o* F7 V! E; O. j+ x │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.mp4
! `. M- s7 y0 p │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.srt
8 i7 g- Y. C# j/ Y8 u │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.txt* _: z" x% U# F' }& L
│ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.mp4) a6 Z; \& n: m& H# D
│ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.srt) E' O' o7 P* Y1 y7 D
│ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.txt
! f R2 i7 G( i! c0 P& t" j │ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.mp4* s7 Q8 Y3 ]. o) P @) C. u( G _1 }
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.srt
! R0 C. q- D' k; V7 a │ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.txt
$ ~! `1 N0 _& r( \. K8 h* E │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.mp4" c$ _" R. [0 w& k( j6 O
│ 4 - 10 - 10 - Demo - Median filter - Duration 0131.srt
l: u& a, [2 L* q. c: M │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.txt
( F; [$ _3 c8 ?+ G: r' L- Z │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.mp4: |4 f5 f) A; m% w6 b0 S0 y
│ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.srt
* t, e! C. r3 D/ e- m │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.txt
; [' S- r2 r4 g3 `! h( ] │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.mp4/ g: v a2 x, c ~; L; c
│ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.srt
5 G! d& a2 l! O9 Y. H$ R │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.txt2 i% B1 k! m3 P, Z
│ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.mp4
+ I# U0 y d( g) X e │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.srt
3 d+ Q" p( w5 L+ _" D$ p │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.txt- ^, E- t$ e, l! _( \
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.mp4# Y' t: q5 J# P
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.srt3 o* B9 p& D6 J/ j0 Z, p+ i7 |4 Z
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.txt
9 Q N+ K. ~: S2 k, W4 x( ?4 i │ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.mp4+ K1 r8 _- t* S, {- B: ^6 }6 i" Q, e
│ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.srt7 j$ |8 H2 z, y4 ~! Z
│ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.txt
# z& `% S; g" S6 T3 }2 W │ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.mp4& U/ Z! a3 |+ @0 `0 T+ y( N& {
│ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.srt
8 D( w0 @ E/ S* Y7 i4 S │ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.txt5 p" D8 k. m- O2 L: [
│ 4 - 4 - 4 - Histogram matching - Duration 0831.mp45 w2 Y# O+ [' f+ _
│ 4 - 4 - 4 - Histogram matching - Duration 0831.srt) Z9 J+ P1 j( z: `8 u% L0 F1 |
│ 4 - 4 - 4 - Histogram matching - Duration 0831.txt
% X( ^9 ~2 u% o! h! S │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.mp4
C+ h+ |/ w3 g5 c │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.srt
) j# X: m5 |9 R4 ^; S │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.txt
" g2 x! T+ y! F# l3 l │ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.mp4# `& ?; C5 f& K/ O8 {
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.srt5 V+ t1 m1 z8 Y3 [ a/ Q9 D" \. A
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.txt# m" c9 Y! X& j" T' `; p
│ 4 - 7 - 7 - Non-Local means - Duration 0727.mp44 V* e+ X2 C- T; F
│ 4 - 7 - 7 - Non-Local means - Duration 0727.srt
9 W6 {6 P) O- ~" Z$ p. ] │ 4 - 7 - 7 - Non-Local means - Duration 0727.txt
+ Q4 ^$ {# P. {; {, n# {3 W │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.mp4
7 f: q( W4 b6 k9 T) g) v │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.srt
9 C* X* _. p, l │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.txt8 H& D* E$ ]) J# U* g
│ 4 - 9 - 9 - Median filter - Duration 0720.mp4
3 M! }/ j9 p( t" G │ 4 - 9 - 9 - Median filter - Duration 0720.srt
8 {4 S( y) _0 N% e6 O: e! R& H7 [' q │ 4 - 9 - 9 - Median filter - Duration 0720.txt: R, {" c1 E7 R" W9 V
│ 5 - 1 - 1 - What is image restoration - Duration 0749.mp4. L2 ?- |" S9 c; q( y% l1 Y: T$ `
│ 5 - 1 - 1 - What is image restoration - Duration 0749.srt; u) s. z- E& |% A. P8 e4 L
│ 5 - 1 - 1 - What is image restoration - Duration 0749.txt
& E- D5 Z0 U* I │ 5 - 2 - 2 - Noise types - Duration 1243.mp4 E7 f$ N: L# F9 G5 s; [
│ 5 - 2 - 2 - Noise types - Duration 1243.srt0 L" P: E- A; F' x
│ 5 - 2 - 2 - Noise types - Duration 1243.txt
$ T7 i: g4 J) f# v │ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.mp4* Y: N( H8 D7 }+ D l
│ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.srt. H2 j/ _8 t ^. [- s1 }" L
│ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.txt( I- n0 |9 ?3 d- v$ n
│ 5 - 4 - 4 - Noise and histograms - Duration 0452.mp49 Q- [; A2 M* K2 f! J& ?3 |& e
│ 5 - 4 - 4 - Noise and histograms - Duration 0452.srt( }7 ^" w5 k' P
│ 5 - 4 - 4 - Noise and histograms - Duration 0452.txt
0 a! W/ q+ W0 L* G* ?; _/ U. H │ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.mp4; ?& }% K! x$ @
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.srt
1 s' w# | X: ^. v6 a │ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.txt
, \0 P1 v7 a) ?* B │ 5 - 6 - 6 - Degradation Function - Duration 1140.mp4
z1 X" e5 [$ ]/ W+ e0 U │ 5 - 6 - 6 - Degradation Function - Duration 1140.srt
1 e4 M; b; s, l; S │ 5 - 6 - 6 - Degradation Function - Duration 1140.txt$ r3 D; F9 \' e2 \+ P( N: ]: k
│ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.mp4
' ^) z: K5 Q$ K6 y# s │ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.srt/ Q7 u# B$ S! S, ^
│ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.txt
2 }: L4 m. a- C) L) @: \ │ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.mp4
$ v* `4 G; G4 k: ^$ m8 Y │ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.srt$ I2 r* `" n6 E% @$ p
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.txt+ J5 D) Q+ h( P$ ^
│ 5 - 9 - 9 - Concluding remarks - Duration 0033.mp4( n, H3 O E. K2 h: e0 H4 X/ \& b$ ^; G
│ 5 - 9 - 9 - Concluding remarks - Duration 0033.srt
: I3 l1 f+ v# _# W8 ? │ 5 - 9 - 9 - Concluding remarks - Duration 0033.txt
: m" h# |/ u8 I5 Q9 Y& E' N │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.mp4( K% f( T. Q/ Q- `4 N8 I+ d
│ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.srt
2 B* }5 o5 ^; f( x, \+ | │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.txt7 Y* C" K$ b3 ~
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.mp4, k& S4 A2 Y/ w4 A
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.srt
1 w3 x; N- Q, X+ K │ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.txt# l Q/ m$ ~6 _$ l3 M
│ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.mp4
1 c; q, B* R3 `. F8 ^: r │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.srt9 Y4 s' v6 d- k. ]
│ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.txt) |- m3 q) Z; C# c/ Y
│ 6 - 12 - 12 - End of the Week - Duration 0021.mp4
# u' y* h' o) }3 Y │ 6 - 12 - 12 - End of the Week - Duration 0021.srt/ S0 ?5 O% b. I; u1 y
│ 6 - 12 - 12 - End of the Week - Duration 0021.txt$ e2 e# Z3 K; S* t' B' A
│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.mp41 e, Z6 {6 X) ? r7 F/ g0 C& u+ ^; m
│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.srt3 c: D4 |1 }6 L) {8 D
│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.txt: a9 B" \. B9 F n. p) c6 ~
│ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.mp41 v* Y* g9 e+ ?5 y" O6 ?+ ]
│ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.srt3 c: v8 j8 r% W0 F
│ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.txt0 Y% x4 V/ f: C9 e1 H
│ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.mp41 h8 i5 I, U! y: r+ F. l
│ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.srt
( v% Q" d" i% N7 T. ]! P │ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.txt8 G2 F9 n9 O: b6 c! r: O
│ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.mp4
6 }" P V+ L+ b0 B. j │ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.srt; q Y3 m; K' |0 I6 I, t& `: o2 |
│ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.txt+ P8 q, m1 z' z% C6 S+ l
│ 6 - 6 - 6 - Congratulations - Duration 0017.mp4
( t" T0 v' }* T7 l0 f% ? │ 6 - 6 - 6 - Congratulations - Duration 0017.srt
/ L* x& G6 a+ y& L6 S$ w( s I │ 6 - 6 - 6 - Congratulations - Duration 0017.txt
; m C/ g9 X! r( ? │ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.mp4
7 b/ I$ Y! M" S3 y U0 [ │ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.srt* M, I$ u$ `" q7 k
│ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.txt* u( D: b5 n9 s0 h* _6 ]. u
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.mp4
/ F) T1 W; A7 } │ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.srt! q, s& n( X. v$ U$ T
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.txt6 |6 V& D; q G4 e
│ 6 - 9 - 9 - Mumford-Shah - Duration 0550.mp48 D$ h( y, ~( t8 ?9 L5 y7 R6 n7 _7 F
│ 6 - 9 - 9 - Mumford-Shah - Duration 0550.srt
5 A# l: C% J+ Y; c$ [ o │ 6 - 9 - 9 - Mumford-Shah - Duration 0550.txt9 Y6 }/ l+ K* `- V
│ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.mp4
; D2 w1 E2 H- P( p │ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.srt$ _' i: g9 f: ~
│ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.txt
! [# \2 l7 h& u. u' F9 E │ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.mp4" D% x- s% e8 x6 T0 Z
│ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.srt( W9 H' V o" V3 s; J# c1 x
│ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.txt
% P5 R( c; h9 _* M- b4 I/ e0 o │ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.mp4/ }# ~. j5 Y6 r: v
│ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.srt1 A, r! ?+ R! ?4 X- c
│ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.txt
3 B: O( y8 R, t a │ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.mp4" D3 ~& M3 |7 {
│ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.srt
0 g. e8 }) `. B5 C+ b/ Z │ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.txt2 Z. O. o$ X3 n" w8 f
│ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.mp4/ `' U! U q9 x
│ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.srt
/ w7 B1 E; M' Y u$ N │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.txt
; J! \- e3 [' k$ \' `8 @1 b6 @' Y │ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.mp4
% |5 i6 s1 w8 g6 o4 \' s+ R │ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.srt8 W! _$ a3 \$ x V$ S+ k6 z
│ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.txt6 h' o' N- m' ?# m
│ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.mp4
6 D. z M3 T9 f │ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.srt
3 e. m" M- F- n, S# b1 Z( b │ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.txt) @- g, w# D+ r* I( H
│ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.mp4
1 O4 A$ J; [% U+ i1 P. E: y │ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.srt( S( E8 L7 X+ b! N4 o' s. X
│ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.txt
2 ^+ z9 L4 u( r+ B: R( y │ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.mp4
, _( [8 `# V, k+ l │ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.srt
" {0 A1 r& a ?3 Z1 b0 | │ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.txt h- C+ M8 |. G& I9 [( V
│ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.mp4' B) n ]4 e: ~. l8 G. B, g
│ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.srt6 ?; g) s9 `0 g7 ]# M i5 z! z
│ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.txt
* A! t1 F, H, F4 q0 U7 l2 A │ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.mp4
, A0 v% ]/ h6 ?; b# G$ h7 _2 v │ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.srt2 N' ~+ f2 G7 x: U3 w$ \6 y
│ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.txt3 V& n& a( H5 u( R0 D
│ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.mp4) I% X* ^8 ^7 N' A% @
│ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.srt) U1 v! g3 ]0 C! X
│ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.txt
$ h$ W+ @" w7 x) c. b/ c& M0 R │ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.mp4
9 C( _, n9 ^0 x( X' U: B$ Q │ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.srt
$ h, i0 g# G. a' z. I │ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.txt* C9 |7 O- C- X
│ 8 - 5 - 5 - Smart Cut and Paste - Duration 0751.mp4/ D; o6 G$ R# ^" r- c
│ 8 - 5 - 5 - Smart Cut and Paste - Duration 0751.srt
: E5 I" y8 [2 r; t8 K+ Q9 r │ 8 - 5 - 5 - Smart Cut and Paste - Duration 0751.txt
0 _; `: J7 {4 r, S4 s/ b │ 8 - 6 - 6 - Demo - Photoshop Inpainting Healing Brush - Duration 0231.mp4
) u1 G/ B+ p! b │ 8 - 6 - 6 - Demo - Photoshop Inpainting Healing Brush - Duration 0231.srt
6 [: @5 P& ^+ k9 Z' q; e │ 8 - 6 - 6 - Demo - Photoshop Inpainting Healing Brush - Duration 0231.txt
) H& V& Q" [' J │ 8 - 7 - 7 - Video Inpainting and Conclusions - Duration 0513.mp4; i+ D! ^4 n& @
│ 8 - 7 - 7 - Video Inpainting and Conclusions - Duration 0513.srt
7 a9 `( L6 e4 l. D4 k, i; x │ 8 - 7 - 7 - Video Inpainting and Conclusions - Duration 0513.txt+ J" K' _2 s7 G; a" G6 |2 n |7 u! [
│ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039 .mp4/ w" q6 ~% Z/ c8 j6 V- |
│ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039.mp4. I( S: ]; o$ |! V) E# |
│ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039.srt
! P' d% Z0 E% j- Z' g │ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039.txt |
所有IT类厂商认证考试题库下载
【新盟教育】2023最新华为HCIA全套视频合集【网工基础全覆盖】---国sir公开课合集
【新盟教育】网工小白必看的！2023最新版华为认证HCIA Datacom零基础全套实战课
原创_超融合自动化运维工具cvTools
重量级~~30多套JAVA就业班全套 视频教程（请尽快下载，链接失效后不补）
链接已失效【超过几百G】EVE 国内和国外镜像 全有了 百度群分享
某linux大佬，积累多年的电子书(约300本)
乾颐堂现任明教教主Python完整版
乾颐堂 教主技术进化论 2018-2019年 最新31-50期合集视频（各种最新技术杂谈视频）
Python学习视频 0起点视频 入门到项目实战篇 Python3.5.2视频教程 共847集 能学102天
约21套Python视频合集 核心基础视频教程（共310G,已压缩）
最新20180811录制 IT爱好者-清风羽毛 - 网络安全IPSec VPN实验指南视频教程
最新20180807录制EVE开机自启动虚拟路由器并桥接物理网卡充当思科路由器
[复制链接]
