8 \6 i _$ B" z3 F7 D/ r ├─001_machine_learning_by_andrew_ng_coursera
7 T! Y8 a+ V: [ │ └─001_machine_learning_by_andrew_ng_coursera
x8 x2 ~. r7 ^9 k4 N │ ├─ml-003+ c( \! v8 j* x7 l2 S$ w3 f( Y$ U& e T
│ │ ├─01_I._Introduction
& a; Y7 L L' g: u/ m1 p, ^' G │ │ │ 01_Welcome.mp4# O3 {; v! z1 g) x8 s! S' p6 x; q& |
│ │ │ 01_Welcome.pdf! K+ `4 H( I1 R% v+ c- F4 b, h
│ │ │ 01_Welcome.pptx
& i' D5 ?4 i7 p% A/ G! w │ │ │ 01_Welcome.srt
4 a% @! X9 K" G: D, q3 @. E8 _4 \) A │ │ │ 01_Welcome.txt
/ \& m9 f8 r# K7 O │ │ │ 02_What_is_Machine_Learning.mp4" f8 e j3 U2 h" e7 E' t
│ │ │ 02_What_is_Machine_Learning.srt
: R/ b, i, y4 U0 c ^8 \ │ │ │ 02_What_is_Machine_Learning.txt
6 C) l8 E% |: O1 r6 P8 j │ │ │ 03_Supervised_Learning.mp4" g5 {7 q/ n3 D! a7 l8 V* H
│ │ │ 03_Supervised_Learning.srt
3 B# C: F0 \0 ]/ ]" I │ │ │ 03_Supervised_Learning.txt
: N/ X1 N7 ]* i( L │ │ │ 04_Unsupervised_Learning.mp49 J/ ^6 a1 P! O7 U
│ │ │ 04_Unsupervised_Learning.srt5 K4 L% B' Y. D
│ │ │ 04_Unsupervised_Learning.txt# G0 h1 v4 O, \# ?1 d
│ │ │
* \ b8 a5 Q2 i1 h9 C" y │ │ ├─02_II._Linear_Regression_with_One_Variable
4 ]5 k8 E8 [; J' A3 g/ P, e6 B$ a │ │ │ 02_Cost_Function.mp4/ X4 _2 o8 w) P, A0 ^5 Q+ |" ]
│ │ │ 02_Cost_Function.srt
0 A" Z5 {$ Q/ L, C0 c: B8 e5 ` │ │ │ 02_Cost_Function.txt
U R! t* M7 I3 P' u2 I6 i │ │ │ 05_Gradient_Descent.mp4
" Y' [9 S! t* S# P │ │ │ 05_Gradient_Descent.srt! ~4 C4 b# S1 [8 `
│ │ │ 05_Gradient_Descent.txt
' W2 @6 \+ }3 g" g* \ │ │ │ 08_Whats_Next.mp41 e. A) A& e( X9 s" R# x2 q
│ │ │ 08_Whats_Next.srt
. \6 m9 \2 ^5 z │ │ │ 08_Whats_Next.txt
5 x- E3 S* q/ @ │ │ │
- T& [) v3 p- R- r4 [5 {9 B3 W │ │ ├─03_III._Linear_Algebra_Review
1 p$ L0 n$ c9 R │ │ │ 01_Matrices_and_Vectors.mp4
& s9 C0 D) O% r │ │ │ 01_Matrices_and_Vectors.pdf! z7 \/ l, n# s3 L) R
│ │ │ 01_Matrices_and_Vectors.pptx
; Q" o7 r! T1 w) Y- V2 e │ │ │ 01_Matrices_and_Vectors.srt* c. c0 P0 @ ^5 v [; ^& H* B
│ │ │ 01_Matrices_and_Vectors.txt x2 p9 y" E/ I- Z
│ │ │ 03_Matrix_Vector_Multiplication.mp4
4 A$ l- I+ j+ k2 B! l6 s3 Q0 N │ │ │ 03_Matrix_Vector_Multiplication.srt
9 d3 K' k |' I, ~, V. q │ │ │ 03_Matrix_Vector_Multiplication.txt; t! w u8 R7 d& @- T4 }7 Z5 g
│ │ │ 04_Matrix_Matrix_Multiplication.mp4
6 z2 W* m' A4 N8 s; e │ │ │ 04_Matrix_Matrix_Multiplication.srt/ A6 u- ?) }& a5 x3 b
│ │ │ 04_Matrix_Matrix_Multiplication.txt
) @8 k0 E+ V2 z" b │ │ │ 05_Matrix_Multiplication_Properties.mp4
6 a( [( [& [; X4 z/ p" q$ O │ │ │ 05_Matrix_Multiplication_Properties.srt
4 K& \3 ^4 Y: v4 ]0 y1 L │ │ │ 05_Matrix_Multiplication_Properties.txt% H, t3 t1 \$ U
│ │ │ 06_Inverse_and_Transpose.mp41 g7 J: T- b. n% Q0 L% ^9 r3 k
│ │ │ 06_Inverse_and_Transpose.srt" C9 c5 @0 {- }1 |' Z0 Z
│ │ │ 06_Inverse_and_Transpose.txt7 w0 ~# m& y3 Y7 D
│ │ │ 1 q' U8 e/ n/ l
│ │ ├─04_IV._Linear_Regression_with_Multiple_Variables" i9 f& I( r. C$ q
│ │ ├─05_V._Octave_Tutorial; m' U u$ T5 r- s6 K1 ]
│ │ │ 01_Basic_Operations.mp4
+ i1 I7 d7 J1 x. z1 D7 \ │ │ │ 01_Basic_Operations.pdf
; P2 Z1 C8 I2 y8 c/ P) w& D1 \ │ │ │ 01_Basic_Operations.pptx
' E- h* R0 P9 Q' K8 N7 j │ │ │ 01_Basic_Operations.srt
5 T7 j# ~& N! a2 {2 O$ O │ │ │ 01_Basic_Operations.txt
1 ~* {" \ n. I3 g' ` │ │ │ 02_Moving_Data_Around.mp4
8 M3 f# s% b' H% a" A │ │ │ 02_Moving_Data_Around.srt5 c) L& t W2 n# }0 M; ?3 z
│ │ │ 02_Moving_Data_Around.txt
7 }0 e7 d/ t7 _9 q4 u* I* { │ │ │ 03_Computing_on_Data.mp4. d% ~4 P% s' c4 ` O
│ │ │ 03_Computing_on_Data.srt
8 n6 c1 B" d; x/ ` │ │ │ 03_Computing_on_Data.txt
; m. Z9 a3 M- Z7 B7 b2 o │ │ │ 04_Plotting_Data.mp4, h# s( _0 h' z: U# k# H
│ │ │ 04_Plotting_Data.srt
; j7 {2 H: K4 C- Q! U │ │ │ 04_Plotting_Data.txt% h9 T& d R. Y; D2 R8 m- N
│ │ │ 06_Vectorization.mp4
# e' ?* [% J8 d │ │ │ 06_Vectorization.srt
. S& | E8 Y8 q" ` │ │ │ 06_Vectorization.txt
- t; ~) ? o; `. m │ │ │ ) u3 q6 o7 r2 c+ W8 t! W2 m
│ │ ├─06_VI._Logistic_Regression2 r" ]. R N# u1 P* g
│ │ │ 01_Classification.mp42 `/ b: G3 z# i2 S5 O" f
│ │ │ 01_Classification.pdf
, D& }' P; L# ? │ │ │ 01_Classification.pptx
/ `. e( k3 e6 A, f" R │ │ │ 01_Classification.srt
2 o4 j) m& A4 o' P+ R" r │ │ │ 01_Classification.txt
# R: }3 p8 t$ c& @7 X9 Z" J │ │ │ 02_Hypothesis_Representation.mp4
7 B( V: T; B$ ?- ^ │ │ │ 02_Hypothesis_Representation.srt* i1 @, \8 A: ]5 E/ i8 B
│ │ │ 02_Hypothesis_Representation.txt7 q; @' `( r' [& o5 \/ g
│ │ │ 03_Decision_Boundary.mp4
: D8 i! S/ y; w4 A5 { [. u │ │ │ 03_Decision_Boundary.srt/ c4 C: |4 V& \( {
│ │ │ 03_Decision_Boundary.txt
5 C+ O4 i5 d- j. c! T9 W v: ?1 b │ │ │ 04_Cost_Function.mp4
1 J& G+ ^9 i6 S) U │ │ │ 04_Cost_Function.srt2 ~7 W$ y4 h+ N. F1 o" C: K; T
│ │ │ 04_Cost_Function.txt ]+ P E! c) Z0 I4 J
│ │ │ 06_Advanced_Optimization.mp4" u( D! B8 [# r* h# V- z
│ │ │ 06_Advanced_Optimization.srt/ w, t5 d& a' {" f% {# m
│ │ │ 06_Advanced_Optimization.txt& m5 O) \$ v- @. [, _
│ │ │
: t; _2 R: Y8 G) Z, s4 r- c; v │ │ ├─07_VII._Regularization& @) P b) @7 N& h" u
│ │ │ 01_The_Problem_of_Overfitting.mp4/ h7 O2 G$ w0 n" U$ ~/ v
│ │ │ 01_The_Problem_of_Overfitting.pdf5 w- _) c8 w+ @
│ │ │ 01_The_Problem_of_Overfitting.pptx I- q) p' Q1 a8 x3 I
│ │ │ 01_The_Problem_of_Overfitting.srt
8 _1 N# @; x; y2 G/ G: q# K │ │ │ 01_The_Problem_of_Overfitting.txt1 ]' j3 q' Q; @* T0 Z! t
│ │ │ 02_Cost_Function.mp4# L5 L- T2 |+ z
│ │ │ 02_Cost_Function.srt
! c, b3 `# `1 V$ ~ │ │ │ 02_Cost_Function.txt
. l' _1 U: ?5 ~ │ │ │ 03_Regularized_Linear_Regression.mp4
5 U, B* g2 h! \6 W7 b# u' H9 @0 [ │ │ │ 03_Regularized_Linear_Regression.srt" `6 J1 N2 o5 O$ h# |& Z% D
│ │ │ 03_Regularized_Linear_Regression.txt$ n+ r: g- Q/ t0 G W1 b5 l* @0 |
│ │ │ 04_Regularized_Logistic_Regression.mp43 o3 n+ j; `) y- F
│ │ │ 04_Regularized_Logistic_Regression.srt
2 J7 ^! w7 P. F% t" P' V │ │ │ 04_Regularized_Logistic_Regression.txt
' O+ ]" ]0 t* t) Z: J' N6 `6 N; m+ m- x │ │ │ ' T* K5 e4 Q+ _% }
│ │ ├─08_VIII._Neural_Networks-_Representation
$ k( n% h8 V# a) E7 I) O; y │ │ │ 01_Non-linear_Hypotheses.mp44 c2 M% P+ P- q3 w. {- ~
│ │ │ 01_Non-linear_Hypotheses.pdf( F8 Q. r& Q! i
│ │ │ 01_Non-linear_Hypotheses.srt
& S* F* E* L$ ] │ │ │ 01_Non-linear_Hypotheses.txt
7 a5 _/ F- d' n0 M. }2 v. s │ │ │ 02_Neurons_and_the_Brain.mp45 A# ?# b E" U
│ │ │ 02_Neurons_and_the_Brain.srt {! I. [ E. F& `6 F2 h5 E0 x
│ │ │ 02_Neurons_and_the_Brain.txt5 }- } d( j+ w, e P( `- R
│ │ │
2 N( E6 ?4 w7 _1 G8 W │ │ ├─09_IX._Neural_Networks-_Learning
0 F1 F: I2 s# e2 }) y │ │ │ 01_Cost_Function.mp4/ H( J0 e' [9 T- o( n9 x1 P2 W8 \
│ │ │ 01_Cost_Function.pdf
) U4 f, m& _. |$ Z; f; P" @, r │ │ │ 01_Cost_Function.pptx3 j X& y" U' P
│ │ │ 01_Cost_Function.srt$ i7 r y' i+ i [/ F$ a
│ │ │ 01_Cost_Function.txt& A6 ~" {' C* W0 X
│ │ │ 02_Backpropagation_Algorithm.mp4& b( j; { K, M q4 @0 X. B
│ │ │ 02_Backpropagation_Algorithm.srt
" [ d0 g4 {; _ │ │ │ 02_Backpropagation_Algorithm.txt
4 U" B* V3 \9 B+ ^ │ │ │ 03_Backpropagation_Intuition.mp49 [3 q0 C2 X0 }& t( H4 ]+ B
│ │ │ 03_Backpropagation_Intuition.srt* T/ f4 n0 ]: y, t
│ │ │ 03_Backpropagation_Intuition.txt( `" A5 V4 s, P9 k5 u
│ │ │ 05_Gradient_Checking.mp4" b7 A( t" i3 q2 u5 C, |
│ │ │ 05_Gradient_Checking.srt
2 I$ P6 U# n5 I9 E; h │ │ │ 05_Gradient_Checking.txt' F/ Y) G6 ~) u* T
│ │ │ 06_Random_Initialization.mp4
7 k! ?4 w8 ~" L# K7 J/ J │ │ │ 06_Random_Initialization.srt
4 Q& L" B- K! ?: _0 s │ │ │ 06_Random_Initialization.txt$ ~3 _) [- w6 S, B; O8 I
│ │ │ 07_Putting_It_Together.mp4
3 Y/ F( e5 {" I* M* W i │ │ │ 07_Putting_It_Together.srt
0 ]# W. d# P8 d7 f: s7 o │ │ │ 07_Putting_It_Together.txt
1 Q1 J: T6 b# f* w* { A │ │ │ 08_Autonomous_Driving.mp4
% r1 a% C0 X/ k; s3 W │ │ │ 08_Autonomous_Driving.srt. B, w9 Q7 s8 y" Q% [0 ]) E- [
│ │ │ 08_Autonomous_Driving.txt
+ F! ~! i$ m% | │ │ │ - \1 S9 T$ h+ S' o
│ │ ├─10_X._Advice_for_Applying_Machine_Learning% M8 }- g/ e) \5 l- \4 L
│ │ │ 06_Learning_Curves.mp4
- j, c( Z" k: l │ │ │ 06_Learning_Curves.srt
) e- z2 @7 l6 P) J$ _( s │ │ │ 06_Learning_Curves.txt& ~, f- ~/ Q% K2 n6 J& _+ |
│ │ │ 2 n& V; C' `3 a" D
│ │ ├─11_XI._Machine_Learning_System_Design
2 n4 E, R! x) ? H. E │ │ │ 02_Error_Analysis.mp4
& v* [$ u9 n' b/ r │ │ │ 02_Error_Analysis.srt
* }: C" G/ O0 L │ │ │ 02_Error_Analysis.txt: V/ i* c6 u% a$ G+ ^2 t' J; S F6 U
│ │ │
* d1 P/ K9 O/ B3 p" ]/ Z: \ │ │ ├─12_XII._Support_Vector_Machines0 t( M* Z4 s- `9 _
│ │ │ 01_Optimization_Objective.mp4
+ ?- w d* Y- M │ │ │ 01_Optimization_Objective.pdf4 h; E- g! j6 ^* j
│ │ │ 01_Optimization_Objective.pptx
: k; c" s5 S L2 o9 k │ │ │ 01_Optimization_Objective.srt3 B- w- _2 h. N ]5 U
│ │ │ 01_Optimization_Objective.txt$ r+ ]8 a* n0 o9 a3 b
│ │ │ 02_Large_Margin_Intuition.mp4* v- K% T0 N) c1 H3 Y$ I( @, V
│ │ │ 02_Large_Margin_Intuition.srt% f$ B% R4 Y, A8 B7 H4 n) d% A9 {
│ │ │ 02_Large_Margin_Intuition.txt
' j& F7 ^8 O) y" Q │ │ │ 04_Kernels_I.mp4
8 E8 @+ \' F) |' t │ │ │ 04_Kernels_I.srt
) y9 Y; t. H4 a# _9 h) r │ │ │ 04_Kernels_I.txt0 z- e" ]4 M% I1 P
│ │ │ 05_Kernels_II.mp4- d! F' W) ^, k( P% @- T) V
│ │ │ 05_Kernels_II.srt5 n% ~4 D. @0 S% Z& d, H
│ │ │ 05_Kernels_II.txt3 V I! k [, ~) N2 u
│ │ │ 06_Using_An_SVM.mp4
/ ^: z8 `( V' L4 W; D: M: D: r9 k# E" { │ │ │ 06_Using_An_SVM.srt" }8 ]2 Q1 _& M1 [* a
│ │ │ 06_Using_An_SVM.txt
2 I, R9 Q* P; I( V │ │ │
# g; C: Q6 Q$ @9 _5 v0 }3 h │ │ ├─13_XIII._Clustering
/ @' C! n5 C& k' I& k: C' h+ @! x7 J$ i │ │ │ 01_Unsupervised_Learning-_Introduction.mp4
0 N! H! s+ I+ l! e' O! y+ U3 T │ │ │ 01_Unsupervised_Learning-_Introduction.pdf2 s+ [; E. p! A( D9 }5 Z( h( q
│ │ │ 01_Unsupervised_Learning-_Introduction.pptx
$ [* v2 m8 l) c' N │ │ │ 01_Unsupervised_Learning-_Introduction.srt! K7 h% y$ x, o8 q! E2 o
│ │ │ 01_Unsupervised_Learning-_Introduction.txt3 O1 Q4 I7 u, @+ d( s7 a
│ │ │ 02_K-Means_Algorithm.mp4
: I: _, e) z a# I5 m, k3 M# Y │ │ │ 02_K-Means_Algorithm.srt
7 p" O8 w; |; F( ` │ │ │ 02_K-Means_Algorithm.txt
! A i3 n8 B2 E. ]4 r) P6 v │ │ │ 03_Optimization_Objective.mp49 e: i# |# K w& O
│ │ │ 03_Optimization_Objective.srt% X) u: T* ^6 s- k6 ]3 }6 a
│ │ │ 03_Optimization_Objective.txt2 \3 T2 N( ^0 N7 d; i% r6 ?
│ │ │ 04_Random_Initialization.mp4$ D& C# e3 H8 [( [; a
│ │ │ 04_Random_Initialization.srt% C; N) L* G/ i1 j
│ │ │ 04_Random_Initialization.txt6 P6 U. c; h+ Y4 q
│ │ │ 05_Choosing_the_Number_of_Clusters.mp43 C! O" e+ M3 R0 j& W. M
│ │ │ 05_Choosing_the_Number_of_Clusters.srt. u: W6 z8 B+ |9 d2 X( \
│ │ │ 05_Choosing_the_Number_of_Clusters.txt
; l; X. o. ?2 U$ n- } │ │ │ ; n- J; O L. D) \7 N1 O
│ │ ├─14_XIV._Dimensionality_Reduction
% Q1 L6 q* d; v) W; b: A │ │ │ 02_Motivation_II-_Visualization.mp4
; r: a, K. N, I! J │ │ │ 02_Motivation_II-_Visualization.srt% S9 a9 `# U; s, \0 B" g
│ │ │ 02_Motivation_II-_Visualization.txt* d$ X* I' A$ n! u5 E/ L2 e9 v
│ │ │ 07_Advice_for_Applying_PCA.mp4* x0 p4 I% @6 y- k$ I" V1 u/ h) r" M
│ │ │ 07_Advice_for_Applying_PCA.srt$ X) M3 Z# v4 j! ?
│ │ │ 07_Advice_for_Applying_PCA.txt+ k( {& M( B a% D
│ │ │ l" D- e7 L- t
│ │ ├─15_XV._Anomaly_Detection; d9 ~$ y( N/ v* ^$ y. \& t7 W
│ │ │ 01_Problem_Motivation.mp47 X s4 o/ d. b C& ]9 b1 Z
│ │ │ 01_Problem_Motivation.pdf
! d+ _& A9 }" f6 }! G* E! m │ │ │ 01_Problem_Motivation.pptx
1 h# h% e" G: A6 c4 p │ │ │ 01_Problem_Motivation.srt
* j9 ~( Z" k" U/ R& G M# t │ │ │ 01_Problem_Motivation.txt% p" ~3 v1 k9 ?# q6 w
│ │ │ 02_Gaussian_Distribution.mp4
, ]2 ^3 _/ ]- U* b* r │ │ │ 02_Gaussian_Distribution.srt
3 Z0 {' S1 Z; F @9 W │ │ │ 02_Gaussian_Distribution.txt6 a% t1 L9 [" b, m0 Z7 c* }! a
│ │ │ 03_Algorithm.mp4
' _3 i( q$ W* ?+ N& n │ │ │ 03_Algorithm.srt
! e/ S; w) y8 _/ a: f! V │ │ │ 03_Algorithm.txt( R! {6 S. N% l' S6 a2 a4 c7 l
│ │ │ 06_Choosing_What_Features_to_Use.mp45 U7 r* S6 j8 ~6 ?
│ │ │ 06_Choosing_What_Features_to_Use.srt
+ l6 }0 r. I/ C" B. J5 ?: J) U │ │ │ 06_Choosing_What_Features_to_Use.txt% i3 y' M' r0 F
│ │ │
4 l6 u' J9 t- z" R( J5 j │ │ ├─16_XVI._Recommender_Systems# _' h. Q# x: O. {# I- U
│ │ │ 01_Problem_Formulation.mp4
; l9 T/ t( \- G& S+ R* Y6 I* y │ │ │ 01_Problem_Formulation.pdf- {3 a6 {& h3 h! L5 a% L
│ │ │ 01_Problem_Formulation.pptx4 H7 Y/ h# z6 g0 @5 M4 ~2 [' t
│ │ │ 01_Problem_Formulation.srt5 I/ N E+ Y" a' b% Z
│ │ │ 01_Problem_Formulation.txt
) ?) D1 n Y2 U8 y5 I │ │ │ 02_Content_Based_Recommendations.mp4$ q- X5 ]3 l' a' l. F: g( v. d' b
│ │ │ 02_Content_Based_Recommendations.srt
# O; }$ e3 H/ }0 y: L │ │ │ 02_Content_Based_Recommendations.txt, L) u- ^1 v. q' R7 A. v
│ │ │ 03_Collaborative_Filtering.mp4' [3 K) |# v, y) x' E, Y
│ │ │ 03_Collaborative_Filtering.srt$ C2 S7 f4 c! N# G5 ]/ F
│ │ │ 03_Collaborative_Filtering.txt0 H, a/ G8 H0 U9 r
│ │ │ 04_Collaborative_Filtering_Algorithm.mp47 p/ w9 @) B/ O7 n
│ │ │ 04_Collaborative_Filtering_Algorithm.srt
1 `" Z0 f1 V6 r │ │ │ 04_Collaborative_Filtering_Algorithm.txt
% V s" n4 Q3 ` │ │ │ 5 u1 V$ H+ q5 ^6 Q. L! ?
│ │ ├─17_XVII._Large_Scale_Machine_Learning
# h! f8 j! f+ m7 }8 n. o0 H │ │ │ 05_Online_Learning.mp45 f+ | F+ y# G; j8 D `! Z
│ │ │ 05_Online_Learning.srt
7 R: V0 ^9 K t! g9 b │ │ │ 05_Online_Learning.txt
3 U6 V* x- W, t1 ~2 x" T6 w) B │ │ │
. @8 j7 D0 F" n8 V D │ │ ├─18_XVIII._Application_Example-_Photo_OCR
: k. C# y' v2 y& r. {% e │ │ │ 02_Sliding_Windows.mp49 v, J/ e" C. O; B4 k
│ │ │ 02_Sliding_Windows.srt
3 M I% J2 \1 k" V$ ~1 `9 t& X │ │ │ 02_Sliding_Windows.txt/ J! T* X4 p# Q
│ │ │ : i2 K/ Q+ ^3 c0 D6 [4 ]- J$ e
│ │ └─19_XIX._Conclusion
' q4 E9 V' S: Z8 y! o* D │ │ 01_Summary_and_Thank_You.mp4/ k" m4 P% ~, l4 o$ ]# r4 o3 ^" [
│ │ 01_Summary_and_Thank_You.srt0 c7 R, T0 m$ a" q
│ │ 01_Summary_and_Thank_You.txt$ @( p, F7 ^1 O& B) D) ~
│ │ # O h$ @/ g8 F$ P x) l
│ └─机器学习-斯坦福-Andrew NG-2012/ s9 {! J5 o% B- C: c7 T. Z
│ │ 下载说明.txt" C e, B2 {: r# u. H: C+ ^
│ │ 关注我们.png- h) R# t3 ?$ U6 m
│ │ 攻城狮论坛=网络技术+编程视频.url
+ h" c) Q: v y: B: V │ │ 机器学习笔记2012_v0.1.pdf7 Y3 g. i$ h- Q
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
4 T6 T' i' B" m │ │
* Q7 _" l. y# Y9 ? │ ├─week 1% s- O, [! l- S6 T5 k, |
│ │ 1 - 1 - Welcome (7 min).mp4$ U- q) _! x% P3 C
│ │ 1 - 1 - Welcome (7 min).srt
1 Z6 V. b9 X% O) {5 N/ h5 M │ │ 1 - 2 - What is Machine Learning (7 min).mp4
' D) J; A8 M3 {1 y" j" o │ │ 1 - 2 - What is Machine Learning- (7 min).srt! J7 W8 X/ n. R+ ?4 ~5 s; o/ _# E
│ │ 1 - 3 - Supervised Learning (12 min).mp4# s4 h: h8 c6 G3 J$ E
│ │ 1 - 3 - Supervised Learning (12 min).srt" q6 i4 k7 c* f$ D
│ │ 1 - 4 - Unsupervised Learning (14 min).mp43 k# ]. f l/ i
│ │ 1 - 4 - Unsupervised Learning (14 min).srt9 r8 a t! \" _3 ~# ]: V
│ │ 2 - 1 - Model Representation (8 min).mp4
" Z' A0 t( h* t, z0 Z, h │ │ 2 - 1 - Model Representation (8 min).srt
( Y3 F9 y: P# V* q$ \- q │ │ 2 - 2 - Cost Function (8 min).mp4
) d# b% s6 M5 w. P! U+ H │ │ 2 - 2 - Cost Function (8 min).srt
. i' I/ ~/ T% ~ │ │ 2 - 5 - Gradient Descent (11 min).mp4
8 @9 { s$ }8 x3 |0 Z1 | │ │ 2 - 5 - Gradient Descent (11 min).srt1 c) d9 h* S8 r; p2 H B
│ │ 2 - 8 - What's Next (6 min).srt
# `! {, I% y! W/ A6 p │ │ 2 - 8 - Whats Next (6 min).mp4
& @9 G E/ T- G( ]1 _8 j3 K0 d │ │ 3 - 1 - Matrices and Vectors (9 min).mp4
( U- S2 g* B) C& E3 a) n │ │ 3 - 1 - Matrices and Vectors (9 min).srt
; ^" t- B! ^ U! h# U │ │
1 B+ h5 P* Q0 X+ y* }4 |; ~ │ ├─week 10
* N8 |7 d7 @( u. E3 t. B │ │ 17 - 5 - Online Learning (13 min).mp4
3 }4 j# T% I6 b5 d4 Y │ │ 17 - 5 - Online Learning (13 min).srt
1 b" j* }2 ]0 ^6 [, I! Y" O │ │ Lecture17.pdf1 s$ f Y( P4 ^& v* y
│ │
h( D- ]/ M7 W │ ├─week 2# `/ e9 p+ n6 h- `/ h4 B
│ │ 4 - 1 - Multiple Features (8 min).mp4
" \; ]- q& r9 S9 a/ i4 O! k │ │ 4 - 1 - Multiple Features (8 min).srt+ l3 o2 x9 |5 @/ o
│ │ 4 - 6 - Normal Equation (16 min).mp4
( A4 C2 ], O9 w$ I2 y( b │ │ 4 - 6 - Normal Equation (16 min).srt) Y4 D4 n9 V" ^( `( j
│ │ 5 - 1 - Basic Operations (14 min).mp4- C' ?2 w( t8 n' D u
│ │ 5 - 1 - Basic Operations (14 min).srt2 t$ U& Q- H) I8 r1 W8 R
│ │ 5 - 2 - Moving Data Around (16 min).mp4 W, I" u9 }" S2 j. D
│ │ 5 - 2 - Moving Data Around (16 min).srt
) @, s# G; ?' E; z- @5 y │ │ 5 - 3 - Computing on Data (13 min).mp4
3 b0 @, f* N) P) q) F0 u │ │ 5 - 3 - Computing on Data (13 min).srt
" y4 u& u) A0 ?1 [' { │ │ 5 - 4 - Plotting Data (10 min).mp4) w& z1 U, U% p! w" y' K
│ │ 5 - 4 - Plotting Data (10 min).srt4 p' \) y; c$ A6 S6 i3 L+ h
│ │ 5 - 6 - Vectorization (14 min).mp4# T. ]# u! B. E) R) U l
│ │ 5 - 6 - Vectorization (14 min).srt
5 C9 k8 V9 B6 w$ X3 K │ │ Lecture4.pdf" T* G+ @0 Y1 B0 c4 a3 g+ b
│ │ Lecture5_octave tutorial.pdf+ d2 x* ~$ A1 H: G
│ │
7 L/ L* g2 U% p: p) p4 Q │ ├─week 3
. o a6 j* ?$ t( x' b6 r │ │ 6 - 1 - Classification (8 min).mp46 b: F& I+ u4 ~" T, Y- g
│ │ 6 - 1 - Classification (8 min).srt
! Y- o: N4 v4 G" |# n3 ? │ │ 6 - 2 - Hypothesis Representation (7 min).mp40 l. a/ E$ o( {7 }1 R& U
│ │ 6 - 2 - Hypothesis Representation (7 min).srt5 P% o: k. s. u; N" o7 I: Y% c* Q
│ │ 6 - 3 - Decision Boundary (15 min).mp4% d# l( \( l2 B6 z
│ │ 6 - 3 - Decision Boundary (15 min).srt- L% a7 T( L* \. L5 a2 W) G+ Z+ D
│ │ 6 - 4 - Cost Function (11 min).mp4; X( g7 | Y( y; r$ o/ q( y
│ │ 6 - 4 - Cost Function (11 min).srt
( r5 S. [4 Q: I │ │ 6 - 6 - Advanced Optimization (14 min).mp4
$ P3 R, j. Z; X* ] │ │ 6 - 6 - Advanced Optimization (14 min).srt
. T3 C: }/ o* E8 V- z# i │ │ 7 - 2 - Cost Function (10 min).mp4
+ B3 H2 q; L3 @% R+ m- S │ │ 7 - 2 - Cost Function (10 min).srt3 R2 s: l) l9 H4 {+ W6 ~
│ │ docs_slides_Lecture6.pdf/ X) ]" `4 D/ _. @6 B
│ │ docs_slides_Lecture7.pdf
+ p) X) G; A- \ │ │ ) e) I8 `* L" P4 \& n& H: l+ E
│ ├─week 44 w9 x$ O6 h1 c. o3 Y2 Y, W
│ │ 8 - 1 - Non-linear Hypotheses (10 min).mp47 B( L" s& z+ C! C
│ │ 8 - 1 - Non-linear Hypotheses (10 min).srt
2 C# u8 k" [ h. C; s │ │ 8 - 2 - Neurons and the Brain (8 min).mp4* i! z4 V1 j5 B; @3 m; P% r
│ │ 8 - 2 - Neurons and the Brain (8 min).srt5 ^, i- Q; w; M; O
│ │ 8 - 3 - Model Representation I (12 min).mp4
5 M0 h* i& E# F │ │ 8 - 3 - Model Representation I (12 min).srt: a, Y6 y4 j& W, ]0 }
│ │ 8 - 4 - Model Representation II (12 min).mp42 a6 N# ~6 v. f/ E9 o7 x- m
│ │ 8 - 4 - Model Representation II (12 min).srt
+ y- K6 p: s* R0 ]) L/ l1 h │ │ 8 - 5 - Examples and Intuitions I (7 min).mp49 h( s/ x9 x7 v- W0 Y
│ │ 8 - 5 - Examples and Intuitions I (7 min).srt: A c, J% o( h( |. z9 B
│ │ 8 - 7 - Multiclass Classification (4 min).mp4& Q/ N9 Z8 ~1 p* |/ S) {3 z% K' Q
│ │ 8 - 7 - Multiclass Classification (4 min).srt
8 Y7 M0 d) B* Y' Z │ │ docs_slides_Lecture8.pdf! g( |5 V' a1 N9 Q7 F: N7 N2 l
│ │
3 E" E0 ~# n& H, h x │ ├─week 5! B. V7 N6 d, S" ]
│ │ 9 - 1 - Cost Function (7 min).mp42 A2 K t7 l W" r3 b
│ │ 9 - 1 - Cost Function (7 min).srt4 i5 c! U1 m. p+ L8 T+ W
│ │ 9 - 5 - Gradient Checking (12 min).mp4. i. ~0 ^# o% h8 a' W1 [. [
│ │ 9 - 5 - Gradient Checking (12 min).srt2 Z& x+ ~) R0 ?7 B
│ │ 9 - 6 - Random Initialization (7 min).mp4
0 |" a% m3 C- ~9 p │ │ 9 - 6 - Random Initialization (7 min).srt/ a6 d) s: H, q6 A* y: |
│ │ 9 - 7 - Putting It Together (14 min).mp4/ E6 K0 O, C: S$ e; i
│ │ 9 - 7 - Putting It Together (14 min).srt2 i+ ]: g* h/ p$ O ~' Z7 @' ?
│ │ 9 - 8 - Autonomous Driving (7 min).mp4
" f! C$ l! E4 e8 }& B' ^& p$ f2 ? │ │ 9 - 8 - Autonomous Driving (7 min).srt
' p. l! p7 K f" R% h │ │ docs_slides_Lecture9.pdf
4 |! ~" e- G: f& P- O3 G) e │ │ 9 m0 k: W2 ~- Y4 f2 m! y
│ ├─week 6
+ M. k$ N( m8 k; h │ │ 10 - 2 - Evaluating a Hypothesis (8 min).mp4
: Z9 s, I8 e, U& D, F/ @ │ │ 10 - 2 - Evaluating a Hypothesis (8 min).srt
: V$ F& @" Z7 w% ^' i7 p: _5 N │ │ 10 - 6 - Learning Curves (12 min).mp4
0 H9 k( F7 O0 H6 z │ │ 10 - 6 - Learning Curves (12 min).srt2 ?# p H- z" C: U! o! o) E0 K7 s
│ │ 11 - 2 - Error Analysis (13 min).mp4* w+ J( w7 R$ w: T/ b/ k
│ │ 11 - 2 - Error Analysis (13 min).srt# q) n/ Z! ~" F7 D. L
│ │ docs_slides_Lecture10.pdf5 ~& v7 f4 T5 l" {
│ │
3 B4 o- n5 a" B: Y- R" b# O) o │ ├─week 7. F. f7 R( H. q, Y4 u4 H
│ │ 12 - 1 - Optimization Objective (15 min).mp4
, x; ^5 o# B# }5 X7 w │ │ 12 - 1 - Optimization Objective (15 min).srt
; r' l1 W! s7 l │ │ 12 - 2 - Large Margin Intuition (11 min).mp4
) Y$ z$ I+ t+ t P │ │ 12 - 2 - Large Margin Intuition (11 min).srt! r$ J; F9 z2 q F
│ │ 12 - 4 - Kernels I (16 min).mp41 g' f7 z, \. }% M$ p
│ │ 12 - 4 - Kernels I (16 min).srt# h; d* a: q' Z$ o; _( W" V& Y
│ │ 12 - 5 - Kernels II (16 min).mp47 F- F; \, z" d( z
│ │ 12 - 5 - Kernels II (16 min).srt
% Y. r {3 E" e; y4 \/ D │ │ 12 - 6 - Using An SVM (21 min).mp4
9 @4 S* S" @; k a! T. A+ S: F │ │ 12 - 6 - Using An SVM (21 min).srt
: k/ T, A# o# J │ │ docs_slides_Lecture12.pdf/ |( U! B4 R) z
│ │
- T7 L+ x; `8 p5 d7 } │ ├─week 8- E7 t% z; K/ y# j0 K
│ │ 13 - 2 - K-Means Algorithm (13 min).mp4
V! B$ x2 f7 o! J1 d$ H# q │ │ 13 - 2 - K-Means Algorithm (13 min).srt ^1 \2 ^/ ]1 N9 n3 r3 l* j
│ │ 13 - 3 - Optimization Objective (7 min).mp4
3 ^- `- V6 y. h. [" s' o, Y# Y │ │ 13 - 3 - Optimization Objective (7 min).srt
/ D& L- `$ [9 c' S1 j │ │ 13 - 4 - Random Initialization (8 min).mp40 W( {2 o2 q9 }4 j8 B
│ │ 13 - 4 - Random Initialization (8 min).srt: h, d' S$ q9 |6 x3 X8 g+ n% Y' z
│ │ 14 - 7 - Advice for Applying PCA (13 min).mp4
' `, J% s' h' z* a3 u7 `* m │ │ 14 - 7 - Advice for Applying PCA (13 min).srt2 m" d& V1 u9 D8 M3 w" i% ]0 ~
│ │ docs_slides_Lecture13.pdf
' f; d l/ k$ _/ M7 D+ @ │ │ docs_slides_Lecture14.pdf
4 I& x; D3 ~0 }+ X& I% ^) |. q │ │ 3 s6 ^9 ?5 K4 b1 u
│ ├─week 9
: ~5 T' n- x) r. Y │ │ 15 - 1 - Problem Motivation (8 min).mp41 |" m- `* _0 H
│ │ 15 - 1 - Problem Motivation (8 min).srt, \' }; A7 f3 H! ~) h. z/ G
│ │ 15 - 2 - Gaussian Distribution (10 min).mp4$ a m: t$ Q3 L; S# t
│ │ 15 - 2 - Gaussian Distribution (10 min).srt
' y( H% {$ }* X# P) O │ │ 15 - 3 - Algorithm (12 min).mp4
- V+ D; x" o7 ^+ Q0 \& w │ │ 15 - 3 - Algorithm (12 min).srt0 u8 `# J9 |; d" h# w% \: ~
│ │ 16 - 1 - Problem Formulation (8 min).mp4
8 s+ V/ {7 h e4 b │ │ 16 - 1 - Problem Formulation (8 min).srt6 S0 F$ P- n1 U- i
│ │ 16 - 3 - Collaborative Filtering (10 min).mp4
" A2 T; x/ J, o+ {; f │ │ 16 - 3 - Collaborative Filtering (10 min).srt
3 x7 h5 h4 B. z n$ D: x4 u8 ~ │ │ docs_slides_Lecture15.pdf+ ?( T% f+ Z `+ e9 g+ _8 i: f) I
│ │ docs_slides_Lecture16.pdf1 ?/ P, _. a! v' W* Z3 J
│ │ ) ?0 ^) C6 k9 H8 p5 }! N
│ └─作业 {# `" i( c- J ~/ n
│ │ ex1.zip
0 F& b+ c( H6 x" ~6 ] │ │ ex3.zip+ B2 ]6 ?0 t$ A
│ │ ex4.zip4 f/ r" @# |+ [2 s4 c' t' u
│ │ ex5.zip2 C; \ T% u" ]- h! X) Q
│ │ ex6.zip# U3 h: x4 e, H4 U1 `. E! k7 _
│ │ ex7.zip
5 F5 B( F; S/ |$ ]' I" i. R9 f │ │ ex8.zip, u) B- `" f" S" u! n( F
│ │ Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe, d4 P" m; Q7 g( h$ Y( c
│ │
6 D) |7 r0 F; ~5 b& K4 a8 A │ └─答案
* q0 N( t7 ?. l. z0 _ │ ├─ex14 F: g1 Q5 V, ~+ z3 \' x* W) O% Q6 v
│ │ computeCost.m
* ?, c. J: [. a1 O* j0 V, ? │ │ computeCostMulti.m' Y8 E G' |$ v7 s6 X
│ │ ex1.m9 d3 w2 s1 Y' e( l
│ │ ex1data1.txt
# \! d+ W9 O! U/ o7 L │ │ ex1data2.txt
1 O- t' h! x' r, X5 E │ │ ex1_multi.m+ g, _( X3 M; w
│ │ featureNormalize.m" B9 | U% M0 V
│ │ gradientDescent.m
' Y0 e' }% d( C │ │ gradientDescentMulti.m, \; l1 } U, m& K
│ │ ml_login_data.mat+ e4 r& I3 k0 @, u2 `6 j3 Y
│ │ normalEqn.m
' Z$ ~- {8 Q( D" B1 v' H2 Q │ │ plotData.m
; G5 ?) W8 {0 m* S1 F+ L+ O │ │ submit.m
7 ?3 T9 u8 j8 `+ ~3 o6 E2 \ │ │ submitWeb.m
+ j4 h4 `: L2 I/ s │ │ warmUpExercise.m
, L. Z* }; \/ H. X │ │ 6 \/ h% E+ I' `5 c1 e1 o
│ ├─ex26 l0 q& n, h5 Z1 h1 c$ V: x8 n
│ │ costFunction.m( ?. F- f, E. E9 [! t! @. m& e
│ │ costFunctionReg.m
5 R1 ]5 p, g2 ?2 N& } │ │ ex2.m
5 o: f) a; h. j' c │ │ ex2data1.txt
0 A- U' r6 v7 T* v. t4 \7 Q │ │ ex2data2.txt& U* u* _! H3 V* b2 ~
│ │ ex2_reg.m/ Z% C0 |6 g& H& p3 ]
│ │ mapFeature.m
7 V4 K7 T" Q b │ │ ml_login_data.mat% @7 J& r0 h, J$ i
│ │ plotData.m2 }. u5 |$ o D
│ │ plotDecisionBoundary.m
5 F: J/ {) S2 T0 p │ │ predict(1).m.baiduyun.downloading
9 @7 ~0 o9 H0 F9 x4 _ │ │ predict.m0 _7 T- }- x% W! N9 P9 ?% c
│ │ sigmoid.m: S' \* ?' z0 v; _9 _
│ │ submit(1).m.baiduyun.downloading. S, h# k" m6 J5 P4 m5 `& }, w8 O7 t
│ │ submit.m
0 w/ b* {! A2 o0 { │ │ submitWeb(1).m.baiduyun.downloading$ y8 N2 M9 `0 B# s4 s/ _2 @# d, t
│ │ submitWeb.m
& q8 G% M3 `. `& W1 X │ │ 9 K2 W- M6 ]' k9 W% o2 M9 }
│ ├─ex3
# r2 I, _ S+ d9 N │ │ displayData.m' @$ C; v j8 R. G; e4 }/ c" S3 c
│ │ ex3(437).m.baiduyun.downloading9 \$ o) F# L0 [& w% c
│ │ ex3.m( }8 m& \! B' ]. C- C# e6 f2 B* O
│ │ ex3data1.mat
( {3 @0 K. g" M5 {+ W: z │ │ ex3weights.mat$ L$ ?; v1 y5 p* T. e z) j( A
│ │ ex3_nn.m
, J- t: I* o, z. v$ {) J* E# i" [% x% l │ │ fmincg.m
! g9 E9 k, O5 @% p4 Y │ │ lrCostFunction.m6 J8 f) V8 _+ w: b) i
│ │ ml_login_data.mat
' M% v3 f7 u, v" x# m │ │ oneVsAll.m; s' L$ y7 e/ v6 i
│ │ predict.m
3 [2 F, ]3 E1 x& p9 ` │ │ predictOneVsAll.m+ j, p% n" @( M$ {) D; `- d9 \
│ │ sigmoid.m e. s! x8 t# e$ t3 w1 Q& d9 N# U
│ │ submit.m+ M$ |0 F# {# b8 Z
│ │ submitWeb.m
8 Z7 t/ I8 _! w, o │ │ + A8 o6 U5 D1 D8 N
│ ├─ex4
6 w2 H% q+ @! ~0 Y2 t1 r │ │ checkNNGradients.m- L4 Y* E9 |! p, b0 `. m
│ │ computeNumericalGradient.m. z3 Q* U; Q4 o3 U! f
│ │ debugInitializeWeights.m, v/ ?% o7 n/ n `
│ │ displayData.m
9 ~- `! c, y" f2 g3 |: \ │ │ ex4.m+ G7 n3 Y9 E R, }+ U+ M! W+ d
│ │ ex4data1.mat
2 G3 q# m1 D' v& P* Y6 R) j │ │ ex4weights.mat
5 X8 A4 _8 T/ T; E- {7 F │ │ fmincg.m; }" I7 g5 b: d6 g8 n
│ │ ml_login_data.mat% h, w4 |1 W6 R" C# m1 |# @! b
│ │ nnCostFunction.m
9 q. A" g% C* F4 `8 P │ │ predict.m
, C2 X3 T3 S2 q" U! P" | │ │ randInitializeWeights.m
6 F1 }' \" ^$ R0 Y( v6 x7 F │ │ sigmoid.m
9 J! W% N6 z$ w( T- V: u% z6 P │ │ sigmoidGradient.m
1 H- Z) o4 r( X6 B0 R │ │ submit.m' g( R$ w- C' q1 j* C8 V0 |
│ │ submitWeb.m: T' J: h% U( b |; E- S
│ │
! d5 k% y! S* ~/ w8 {) B │ ├─ex59 Y8 \7 O* Q- ]+ ]- X
│ │ ex5.m
; x5 |. [1 o, L- v) `7 A │ │ ex5data1.mat
2 `2 ], }1 \( e │ │ featureNormalize.m" e. v3 O7 n$ u# F; S1 X
│ │ fmincg.m+ Q6 W w/ S% o4 @2 j, ]
│ │ learningCurve.m
% w. h/ z7 `8 y) }% l4 q) N8 |9 T0 Q │ │ linearRegCostFunction.m1 |3 @; F5 ]9 m/ X- l' i* B$ H
│ │ ml_login_data.mat6 n! y# H9 f1 J+ y6 Z# ~5 N
│ │ plotFit.m: k, }3 t& S! F' p- s9 Z1 g5 M1 ]
│ │ polyFeatures.m
0 E/ u' s3 j6 g9 c+ W │ │ submit.m: P# H# d ?4 |8 @& H6 q
│ │ submitWeb.m/ G6 m4 o% ^ ?( c
│ │ trainLinearReg.m' T" {% F3 a" e1 d7 S4 m
│ │ validationCurve.m
7 N" P& t- E5 ]( C1 ^$ F- `8 b │ │ 9 B: Y' m* y" `+ s
│ ├─ex6
( I# @7 w) Y3 w) T$ u' f8 F: z │ │ dataset3Params.m6 a6 \: y1 y% _; q1 X, e
│ │ emailFeatures.m
; L4 E1 f1 Q- p │ │ emailSample1.txt
. a/ z4 A! x v$ ~+ `7 x │ │ emailSample2.txt
1 A Z" @2 w4 X7 D% S5 L │ │ ex6.m
% k% j% w8 l! t! U# n$ }% R5 P! S │ │ ex6data1.mat# i9 o3 v2 U/ y
│ │ ex6data2.mat
6 z, F5 C7 p( \# j │ │ ex6data3.mat
5 } l' h* [+ A, K* q0 K0 [5 U. d5 { │ │ ex6_spam.m
2 U4 \" O e' B3 U5 c" ~ │ │ gaussianKernel.m# m& o7 j0 w G. ]+ U
│ │ getVocabList.m
$ d3 I" j; J7 ]+ Q: N% U │ │ linearKernel.m8 T Y8 Y$ I& _. [( B& |5 F/ ?
│ │ ml_login_data.mat, C+ P) _5 N0 C
│ │ octave-core
. T1 T: }( }: v% H' U │ │ plotData.m8 D0 g; t. S/ y' t. B
│ │ porterStemmer.m- r: i5 i' V4 E0 ], b# w" O
│ │ processEmail.m& n: `& k7 }& X8 f
│ │ readFile.m
3 B2 L) z- z) c" }/ }: C │ │ spamSample1.txt2 M! P; d( z, D" g! _* H) P9 v
│ │ spamSample2.txt& U3 d; r7 ?2 O; S
│ │ spamTest.mat. ?& ?6 f. G' v6 {
│ │ spamTrain.mat4 J% x/ d0 U; J2 P. p
│ │ submit.m2 r9 I: @0 t6 v" O
│ │ submitWeb.m7 j: S2 o. c6 r* v8 k8 M
│ │ svmPredict.m) E# ?! s+ I% f1 m7 S
│ │ svmTrain.m
7 ]# t* e& w2 s+ z4 w$ `8 R │ │ visualizeBoundary.m
4 C9 N4 z' M, [- F; G, S2 k. z& [) w. m │ │ visualizeBoundaryLinear.m
' e% D5 N$ ]9 p; k5 P1 r │ │ vocab.txt
$ s1 f: n( }: n │ │ - @) L% k; t( T
│ ├─ex7
0 Z* w- X, p* ` O" @ │ │ bird_small.mat
- {9 X! c: ?& c$ ^0 U* t) m* t │ │ bird_small.png3 N5 ?" }: n% N- X' J0 n
│ │ computeCentroids.m) }9 G, Q3 J2 X$ U0 ]( i- o
│ │ displayData.m5 P S8 h+ E% Z# |8 `! \. _
│ │ drawLine.m
* J7 l5 D! Z S │ │ ex7.m
) a K3 l2 P2 B0 [# r+ n8 J │ │ ex7data1.mat
, D" V2 i% q1 X! R │ │ ex7data2.mat
! J. C0 i/ C0 v │ │ ex7faces.mat1 p- b% E: V* u7 W" W \% X
│ │ ex7_pca.m F9 v+ v5 e3 [: @& t t/ N
│ │ featureNormalize.m( Z% K3 T' Z$ e
│ │ findClosestCentroids.m8 u+ Z8 q( [" [1 e% s
│ │ kMeansInitCentroids.m
4 i p0 E! G1 {* J* l& n │ │ ml_login_data.mat
{0 P; r1 Y) ^! d% k; B: p0 \ │ │ octave-core* u9 }8 M9 d4 }) H7 h* @9 o! u& b) G
│ │ pca.m
s5 C. T# ~1 `; b D5 \0 o9 q │ │ plotDataPoints.m% ^3 Q, m4 U+ ~( J
│ │ plotProgresskMeans.m
, N( T& P6 \& M! |9 t& S$ V │ │ projectData.m' R1 B; i8 A/ [+ s: F6 b1 [
│ │ recoverData.m1 k. d- m+ x7 |4 R) V1 T" a @. ?6 m
│ │ runkMeans.m
3 m( z- u& e# V- b) C: w │ │ submit.m, v3 i7 S2 U7 v6 o
│ │ submitWeb.m
% [/ \. w4 e! O4 U+ K# }7 A* t │ │
9 j4 N/ n$ j# i/ | │ └─ex8% a+ j% |0 @& T- @
│ checkCostFunction.m
' n& b" N' E9 g1 s* W s# x/ n' K2 v │ cofiCostFunc.m
1 ]% F0 T( ~ i& v) g/ b# }) o7 a │ computeNumericalGradient.m
) m. d2 C+ D9 `2 m) M" |9 J" R* e │ estimateGaussian.m
/ Z/ v1 W4 c$ v& f+ s │ ex8.m; i/ n/ l/ H5 q
│ ex8data1.mat
0 S0 n/ ~0 j; F8 F) ? │ ex8data2.mat
( G1 F1 r1 ], n. g* W2 ` │ ex8_cofi.m
+ u/ M! y( C9 S. m( W1 e* G │ ex8_movieParams.mat
( A: I; Q0 i) a; K/ O0 z, r │ ex8_movies.mat
9 P" a4 `* J B; S( e7 M │ fmincg.m
! |$ R& D9 m" D% U) A$ h$ h5 x9 }4 B2 K! u │ loadMovieList.m, C3 m5 Z. S% J
│ ml_login_data.mat
- U; ]8 A3 ?$ I2 m5 w) l# j │ movie_ids.txt
Q- C; S; ^" ~. s; [2 x$ R │ multivariateGaussian.m
- z6 v$ N( b! v7 F7 ? │ normalizeRatings.m
9 v# @* Z" d* k. j, j' R' l │ selectThreshold.m6 n0 b* H9 |6 F q6 M2 p0 d
│ submit.m& F5 z9 L" e ^. u% p5 {! [; g
│ submitWeb.m2 K% V* y/ d6 C* w9 I1 ]+ z" L7 `
│ visualizeFit.m
/ [7 S7 H# g/ D6 M │
P# d* `4 a+ U# t* v! u ├─003_机器学习基石
. l' h! Q" S) r" [ │ └─003_机器学习基石; F d2 \1 O. T; C
│ │ 下载说明.txt
. j: W$ N% Y- }2 n │ │ 关注我们.png
( F) |" P: Z- O/ ?+ {3 W$ `9 s │ │ 攻城狮论坛=网络技术+编程视频.url7 @0 ^' s z0 y8 i: i
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt2 i; q( I( u" j1 M: D0 {0 P, N: ^8 f
│ │
- q* j. h, g; o │ ├─Machine Leaning Foundations - TNU# \1 t9 E( x4 E1 C7 p- g) ~+ T
│ │ 01_handout.pdf' c4 G& l) x9 A- G2 W
│ │ 02_handout.pdf
& O0 }5 a7 K! |. B% ? │ │ 03_handout.pdf
# R" Z; a( M" ] │ │ 04_handout.pdf
N; g: w) P6 C, ~ │ │ 05_handout.pdf
& q, K8 Y& G& W% S: L( K │ │ 06_handout.pdf
) d o" E) a S+ C- Q4 K │ │ 07_handout.pdf
' s. g v- }3 T │ │ 08_handout.pdf
5 P% x) m0 Q; E% m7 O5 }7 p0 G6 T7 R3 P │ │ 09_handout.pdf
& @$ c9 i2 Z# _, z │ │ 1 - 1 - Course Introduction (10-58).mp4( g$ W' Q2 r8 ^. J; |
│ │ 1 - 2 - What is Machine Learning (18-28).mp40 Z$ {% A' N9 _; v
│ │ 1 - 3 - Applications of Machine Learning (18-56).mp4
0 ]7 a, ?$ v7 A* d/ Q) w+ d │ │ 1 - 4 - Components of Machine Learning (11-45).mp4
# _5 ?" }! B8 K5 j5 F │ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4
8 Z: w; v) a i& J5 [5 O │ │ 10 - 1 - Logistic Regression Problem (14-33).mp4 k0 z" {* |; v! E% \/ o# `/ r
│ │ 10 - 2 - Logistic Regression Error (15-58).mp4. Z& h7 W1 i9 t3 _( ?& Y! o* g
│ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4 O1 ?6 y7 k/ v2 [4 D
│ │ 10 - 4 - Gradient Descent (19-18).mp40 W# l3 Q8 M6 e. T1 s
│ │ 10_handout.pdf1 G+ l" _% Y2 J7 Z! D
│ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4
# X. V: }1 d1 b1 n6 m; b1 U( r │ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4- {+ Q4 g8 R' e1 P" ^
│ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp43 m; ~/ W6 r( a# V$ ^! d
│ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4
# p8 D3 v1 ~6 ?4 s' |& g4 M │ │ 11_handout.pdf5 j9 d' S u; Q- o
│ │ 12 - 1 - Quadratic Hypothesis (23-47).mp4
+ @0 a& b9 `1 h/ z& E/ w' ] │ │ 12 - 2 - Nonlinear Transform (09-52).mp4& D/ m3 I) N2 b0 K8 I
│ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp49 N6 o+ L" c; P( I* C
│ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4
; w9 }3 x: c' F3 ] P! H │ │ 12_handout.pdf$ Q5 c5 q+ ^ P" N
│ │ 13 - 1 - What is Overfitting- (10-45).mp4
. K) J( R5 k( t' k2 O │ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp40 j1 x6 z. f' h% X3 K4 O
│ │ 13 - 3 - Deterministic Noise (14-07).mp4
4 E: D- U h1 s( E2 m │ │ 13 - 4 - Dealing with Overfitting (10-49).mp4+ b. W& e. t8 Z& j0 G
│ │ 13_handout.pdf
9 d2 a* f0 x1 _! Y# R' s+ l& m- v │ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4
6 i; A% x% U! P8 s- I. ` │ │ 14 - 2 - Weight Decay Regularization (24-08).mp4 ]; P; I- \! I, f! m$ R1 J. P
│ │ 14 - 3 - Regularization and VC Theory (08-15).mp4& r" T5 o, t$ V. w3 [3 H
│ │ 14 - 4 - General Regularizers (13-28).mp4
% u9 n. j* j: ?2 [/ }$ Y │ │ 14_handout.pdf
, N' H! F5 l% W n O) m │ │ 15 - 1 - Model Selection Problem (16-00).mp4
6 [$ \4 X7 c3 M1 N1 S, p9 G" W │ │ 15 - 2 - Validation (13-24).mp4
, }9 n! |2 {& D& \4 F. u2 x │ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
5 y# A) m& m+ d# [/ ^ │ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4
/ L, p2 M) Z- I- J3 s* V │ │ 15_handout.pdf; x* T/ A% @4 Z' U( a
│ │ 16 - 1 - Occam-'s Razor (10-08).mp4' p7 _! P3 S/ F' a1 m# m1 i( m( \
│ │ 16 - 2 - Sampling Bias (11-50).mp41 h9 q* E8 c- L! a6 u" [: Q5 N
│ │ 16 - 3 - Data Snooping (12-28).mp4
1 G( y+ L* F! j. D w! \2 P4 Z │ │ 16 - 4 - Power of Three (08-49).mp4
' F. Z, ?5 V) @ t1 y/ ~ │ │ 16_handout.pdf8 {3 f L9 |' V
│ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4
* @+ L& Q2 Z' c/ h │ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp47 p0 m% n( S( K7 G: ~
│ │ 2 - 3 - Guarantee of PLA (12-37).mp4; L8 A- W: W/ s4 Y) y
│ │ 2 - 4 - Non-Separable Data (12-55).mp49 f6 Y1 e" O/ ?. }) d7 h
│ │ 3 - 1 - Learning with Different Output Space (17-26).mp4' u* \5 r; }9 M; d( T5 {' k1 A- K! M
│ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
^' [4 H1 E, }0 x1 N% O d& j8 A │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4
" {( s9 D0 C0 Y) w │ │ 3 - 4 - Learning with Different Input Space (14-13).mp45 r: F( I/ e1 L
│ │ 4 - 1 - Learning is Impossible- (13-32).mp4
. Z# c! ?0 @( K& j2 g │ │ 4 - 2 - Probability to the Rescue (11-33).mp44 M- E) N- {; ~8 ~& [- ~
│ │ 4 - 3 - Connection to Learning (16-46).mp4
5 }* K2 \ B% v │ │ 4 - 4 - Connection to Real Learning (18-06).mp4; E0 p: Y8 b5 c* x: X, V. B9 e
│ │ 5 - 1 - Recap and Preview (13-44).mp4
: o+ h! @1 [, s1 k9 P │ │ 5 - 2 - Effective Number of Lines (15-26).mp4: w+ @" ^) `8 G9 ?. ]3 H% f5 u
│ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp4
& `, a; s g+ F o1 I! n │ │ 5 - 4 - Break Point (07-44).mp4
- w- h/ V# J/ `0 v& |) J. C │ │ 6 - 1 - Restriction of Break Point (14-18).mp4
6 }0 }; e0 _) M4 l# @ │ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4
! _5 \, K$ ~. V$ u& X │ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
/ ^# s T0 p3 F+ e% t7 _9 A │ │ 6 - 4 - A Pictorial Proof (16-01).mp4
/ C, N: n/ N; {5 K │ │ 7 - 1 - Definition of VC Dimension (13-10).mp4
4 U3 ~$ p) J: u& y │ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp43 r X2 s) H& M2 c
│ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp40 o. Y$ d' F+ J' t7 v
│ │ 7 - 4 - Interpreting VC Dimension (17-13).mp48 m9 w1 T8 d/ n* P
│ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4
+ |. V! D5 e* } │ │ 8 - 2 - Error Measure (15-10).mp47 q2 ~. \$ [# j
│ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4" e y9 n$ S M5 c
│ │ 8 - 4 - Weighted Classification (16-54).mp4
1 j) n2 I' O* d │ │ 9 - 1 - Linear Regression Problem (10-08).mp4
# ~/ E2 |/ ~: }" }5 N │ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4) {+ W6 C" w9 x& _- S
│ │ 9 - 3 - Generalization Issue (20-34).mp4
* F: n) d4 @% E* n/ S │ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp48 a; Z9 j3 [: h! N- X' |. `% y$ b
│ │
! y& V1 f* c) ~1 `' D │ ├─机器学习的基石
2 ~$ ]# E3 {& P0 v │ │ 1 - 1 - Course Introduction (10-58).mp48 k" t. k( L1 r( Q; B) V/ M
│ │ 1 - 2 - What is Machine Learning (18-28).mp4 u& l! h7 d3 T% Y& a; Y
│ │ 1 - 3 - Applications of Machine Learning (18-56).mp44 n- I( o; Q+ u7 i* `$ T3 M
│ │ 1 - 4 - Components of Machine Learning (11-45).mp4
5 K- |$ a; K) ?2 ]* r. H+ c │ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4& N+ B% M w, g U6 }
│ │ 10 - 1 - Logistic Regression Problem (14-33).mp4
1 ~9 l3 I% [% Y$ D │ │ 10 - 2 - Logistic Regression Error (15-58).mp4
+ r, h/ p2 y e& }, J │ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4
9 D/ i) ]' }/ D/ v │ │ 10 - 4 - Gradient Descent (19-18).mp48 x7 f! q! F I9 i/ K
│ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4- q/ g: a1 w/ _2 u0 q7 `
│ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4! I" o& q5 x( c# ?7 Y, D4 Y# L! `
│ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp4
: B, y4 A" B/ W+ F- F │ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4
# j" C! M2 q! a: y6 m │ │ 12 - 1 - Quadratic Hypothesis (23-47).mp4. C/ ~# h( K6 }4 R6 ?4 F8 j9 e# E
│ │ 12 - 2 - Nonlinear Transform (09-52).mp49 |; g: [5 C. }1 a6 s8 \. t, h
│ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp4
( e& H9 E6 _' ^9 }! E │ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4
/ l. J4 Q" W# F │ │ 13 - 1 - What is Overfitting- (10-45).mp4
# t* u5 X4 Z" E9 n7 I' o │ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4
' K8 i U( w4 k( [3 i5 z% {2 S │ │ 13 - 3 - Deterministic Noise (14-07).mp4
% c) O' @7 y. U5 g$ W1 |0 Q+ z │ │ 13 - 4 - Dealing with Overfitting (10-49).mp4% O' @- M; R6 N* O
│ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4$ |. j) N! ]: K4 u, f% Y9 i1 D! q
│ │ 14 - 2 - Weight Decay Regularization (24-08).mp4
$ |5 E g! b( `# L) N5 w4 V, K │ │ 14 - 3 - Regularization and VC Theory (08-15).mp4
( c3 V( k; m' { │ │ 14 - 4 - General Regularizers (13-28).mp4
# T3 @6 x6 Z" T; c7 u: D8 n/ v( ` │ │ 15 - 1 - Model Selection Problem (16-00).mp4* G) r8 d* H0 y' c1 t& u
│ │ 15 - 2 - Validation (13-24).mp4. m3 Y$ B, e3 T+ `
│ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
+ Y' J* |- d* p │ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4 p# ]. c6 R0 r3 ~& ]+ c6 q+ P, }
│ │ 16 - 1 - Occam-'s Razor (10-08).mp4
$ y9 a+ {4 b7 h) z; b; d% X │ │ 16 - 2 - Sampling Bias (11-50).mp49 k2 W7 @' z. X' Z/ @
│ │ 16 - 3 - Data Snooping (12-28).mp41 k4 ]' Z; A! w" Y7 c9 t2 q
│ │ 16 - 4 - Power of Three (08-49).mp4' f9 z9 g1 j$ B% b; j
│ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp42 {8 B8 a- z4 o. ~1 r9 L, w) w
│ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp47 A2 @* ]& D/ v) e, I ]. i
│ │ 2 - 3 - Guarantee of PLA (12-37).mp4, B% y2 n7 y2 E- S+ y5 S
│ │ 2 - 4 - Non-Separable Data (12-55).mp42 u) V& A, z! q# m
│ │ 3 - 1 - Learning with Different Output Space (17-26).mp4
4 R1 K( k2 \6 F& K4 h+ y! B& G8 n$ n │ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
5 H, ]2 P/ Q l │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4
" e5 I4 l/ m8 @+ d9 w C │ │ 3 - 4 - Learning with Different Input Space (14-13).mp4
6 L4 n3 g/ j1 W% o' B9 P1 o │ │ 4 - 1 - Learning is Impossible- (13-32).mp4
% i2 A0 Y* u- @1 p, Y │ │ 4 - 2 - Probability to the Rescue (11-33).mp4
- Y" }: R/ p) f' M; X6 e │ │ 4 - 3 - Connection to Learning (16-46).mp4
) v$ {# a( L; K8 \" e; k │ │ 4 - 4 - Connection to Real Learning (18-06).mp48 }3 E' H$ ?" j1 \( M
│ │ 5 - 1 - Recap and Preview (13-44).mp4& U2 G( c* a2 d$ w- {
│ │ 5 - 2 - Effective Number of Lines (15-26).mp4
9 b) Y$ e; [+ L- h1 M2 n2 |0 L │ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp4# W% Q1 \. U" R+ \9 i
│ │ 5 - 4 - Break Point (07-44).mp4
5 @: Z& G( ~1 a& y$ a │ │ 6 - 1 - Restriction of Break Point (14-18).mp4' a& M: ]$ W8 v$ w- c% x! a6 U# t
│ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4
( J: i6 U: h9 p8 P1 w6 B) Y1 F! M │ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
) Z$ A9 H2 N+ V │ │ 6 - 4 - A Pictorial Proof (16-01).mp4$ `- M, u. i* T, s6 S- Y
│ │ 7 - 1 - Definition of VC Dimension (13-10).mp4* ]5 Y9 E& ?1 T% p* e$ k
│ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp4
1 B9 q) U* t9 I8 d/ n6 z │ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4
# p: A" |: ~1 [/ Q: n7 a' J │ │ 7 - 4 - Interpreting VC Dimension (17-13).mp4( G; Z$ U+ Y9 E# t2 [
│ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4. e" p$ v# I1 y# i
│ │ 8 - 2 - Error Measure (15-10).mp4
& v' ?; c: G' Y8 O& { │ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4; @/ Q; q8 d& `+ Y9 v2 d3 g! E1 A
│ │ 8 - 4 - Weighted Classification (16-54).mp4/ ]1 Z& H/ m, R
│ │ 9 - 1 - Linear Regression Problem (10-08).mp40 G9 w' g: ^( P+ y, F& b/ c
│ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4 T- G9 ?! ^' D' p0 Z( Z
│ │ 9 - 3 - Generalization Issue (20-34).mp4
) [% x6 l! ^( l0 a7 m- N8 U │ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp4
* p6 j/ o" j! r1 P' [ │ │ lecture_slides-01_handout.pdf0 K/ n* A+ C/ |! ~. S$ m6 V
│ │ lecture_slides-02_handout.pdf: d) T: v5 I2 z( A! w8 y a& I
│ │ lecture_slides-03_handout.pdf! L% {( x; x1 l0 k/ t" i+ x7 H
│ │ lecture_slides-04_handout.pdf
& }2 z% d* J9 f# p+ Z │ │ lecture_slides-05_handout.pdf" v4 A! A; [) p2 Q
│ │ lecture_slides-06_handout.pdf
2 ^5 n- a' o) O8 S5 m9 z* ? │ │ lecture_slides-07_handout.pdf
* E% c' ?2 M7 b; H │ │ lecture_slides-08_handout.pdf5 u# {9 P6 X! L- P B
│ │ lecture_slides-09_handout.pdf6 W& i9 [3 j9 m# [% i; K. j
│ │ lecture_slides-10_handout.pdf7 v2 M( L& F" S
│ │ lecture_slides-11_handout.pdf+ h2 d$ V o9 H3 l, k" a
│ │ lecture_slides-12_handout.pdf
: Y! i% p; E$ r+ R │ │ lecture_slides-13_handout.pdf3 C4 [2 S. N% k# p! M1 \
│ │ lecture_slides-14_handout.pdf1 V" K$ W' I1 K6 w
│ │ lecture_slides-15_handout.pdf
' I2 Q; h7 Q5 v; {6 R │ │ lecture_slides-16_handout.pdf
. _! @& F1 @, { │ │ 6 S" {# O/ y3 P
│ └─機器學習基石
* M. I9 x4 q$ u │ ├─01_-_The_Learning_Problem) o$ ], e; O1 T7 \0 T& t2 ^$ r
│ │ 01_Course_Introduction_10-58.mp43 Y0 D, Z. B: i( r+ b( R% J+ V
│ │ 01_Course_Introduction_10-58.pdf6 I7 ]. r" }( t# A# Z
│ │ 02_What_is_Machine_Learning_18-28.mp47 ]6 V- ^" z( \6 w0 j0 Z6 A3 W
│ │ 03_Applications_of_Machine_Learning_18-56.mp4" O/ H) V: n$ ]/ Z. X8 K! f1 g
│ │ 04_Components_of_Machine_Learning_11-45.mp4' B0 M; ]0 X- K$ G* G0 e
│ │ 05_Machine_Learning_and_Other_Fields_10-21.mp4
% V0 _& |6 x; e3 @ │ │
6 r- E. _. ?, K" c" G' K │ ├─02_-_Learning_to_Answer_Yes-No) |1 I9 _5 a( D# v7 V( c& v8 Y# A C8 u
│ │ 01_Perceptron_Hypothesis_Set_15-42.mp4" |2 Y- R0 k5 N1 g% w/ T! S5 x% m
│ │ 01_Perceptron_Hypothesis_Set_15-42.pdf
, T8 j$ U* P& V" W/ L8 z │ │ 02_Perceptron_Learning_Algorithm_PLA_19-46.mp4) ?8 w2 p! p( L8 G5 a" F2 j" M- {* Q
│ │ 03_Guarantee_of_PLA_12-37.mp4; d) \# F: u0 V* M; a
│ │ 04_Non-Separable_Data_12-55.mp4- K: _! c, C! W/ n+ T: k: K! V- h3 e
│ │ 9 m% x/ x) p! a' V
│ ├─03_-_Types_of_Learning
8 _! \' \4 K9 T h" T │ │ 01_Learning_with_Different_Output_Space_17-26.mp49 {2 u4 w0 |, n, f
│ │ 01_Learning_with_Different_Output_Space_17-26.pdf
: H/ j4 `' j7 B x% k- z' R │ │ 02_Learning_with_Different_Data_Label_18-12.mp4
! W4 s% T+ i2 _0 G, @ I │ │ 03_Learning_with_Different_Protocol_11-09.mp4
8 e, d; J W( Q U2 H │ │ 04_Learning_with_Different_Input_Space_14-13.mp47 W- ?. b7 Y3 b
│ │
: c4 }0 u+ l+ U0 A │ ├─04_-_Feasibility_of_Learning4 z% n) f: [1 p. ?. v* x2 c5 B
│ │ 01_Learning_is_Impossible_13-32.mp4( i* A" [: L$ A8 v4 D8 d2 J/ x
│ │ 01_Learning_is_Impossible_13-32.pdf; s* k, v4 L+ k8 J, P$ _
│ │ 02_Probability_to_the_Rescue_11-33.mp40 b; V3 G% z9 f, e3 j
│ │ 03_Connection_to_Learning_16-46.mp4
! k8 a7 u( O' i. f │ │ 04_Connection_to_Real_Learning_18-06.mp4
1 G7 z0 O# P w5 @; R; m" P. } │ │ p( O( u5 ]. [: m, e; I( Z, V
│ ├─05_-_Training_versus_Testing1 {! ~) d, O+ P7 K
│ │ 01_Recap_and_Preview_13-44.mp4" G% g9 k9 j1 h9 n& o2 N' _
│ │ 01_Recap_and_Preview_13-44.pdf
9 [5 X( b' I& m+ { t │ │ 02_Effective_Number_of_Lines_15-26.mp4" F+ I3 u3 j, e( X+ ^8 L
│ │ 03_Effective_Number_of_Hypotheses_16-17.mp4
% g- D$ `/ v+ y& J' C │ │ 04_Break_Point_07-44.mp4
7 ?8 f" r4 g1 I3 t │ │ ) i2 w: W3 {; c: `5 \8 s' _# M
│ ├─06_-_Theory_of_Generalization2 G! A. r z) L$ l
│ │ 01_Restriction_of_Break_Point_14-18.mp4& P5 P: ]0 Z( ^/ I
│ │ 01_Restriction_of_Break_Point_14-18.pdf
1 t9 g( q f! m) ` │ │ 02_Bounding_Function-_Basic_Cases_06-56.mp4
: O4 Q( \" L+ N& p" }, _ │ │ 03_Bounding_Function-_Inductive_Cases_14-47.mp4
2 ^8 i1 {# w& C" W% Y Z: ^ │ │ 04_A_Pictorial_Proof_16-01.mp4
7 o; G# m0 r) a5 l, k% v" @ │ │ / `# G1 p/ t( {% [- R+ e
│ ├─07_-_The_VC_Dimension" ^$ W Y- b; q3 }8 U* d: @
│ │ 01_Definition_of_VC_Dimension_13-10.mp4
/ L+ m$ A7 o6 R" A) f1 n8 b │ │ 01_Definition_of_VC_Dimension_13-10.pdf
- R- u) G. h2 g; H5 S# q: d │ │ 02_VC_Dimension_of_Perceptrons_13-27.mp46 t/ ?9 [1 [' H- x: s/ F& C
│ │ 03_Physical_Intuition_of_VC_Dimension_6-11.mp4( S* K! h( S; @" A) Q/ f
│ │ 04_Interpreting_VC_Dimension_17-13.mp4
- i) h. @6 Y4 g: [7 r% o │ │
. D" ^7 K! q- z( H) d. ~2 s: f- }8 u │ ├─08_-_Noise_and_Error0 N' }5 D; u% Z, Q& x
│ │ 01_Noise_and_Probabilistic_Target_17-01.mp4: O) y6 f1 K4 p% T& E! {( u( @8 H
│ │ 01_Noise_and_Probabilistic_Target_17-01.pdf
8 m! F5 w6 f4 y │ │ 02_Error_Measure_15-10.mp4
0 ?; j( s) R& y: g! p7 n, }( \ │ │ 03_Algorithmic_Error_Measure_13-46.mp4
0 }& ~) {: W2 x9 s6 h" L; [: R │ │ 04_Weighted_Classification_16-54.mp4$ Y* D9 ?6 [; a8 R, I8 @! f
│ │
[. L+ \/ a1 b- k; t │ ├─09_-_Linear_Regression, V. e& [! v4 F8 }6 M" W
│ │ 01_Linear_Regression_Problem_10-08.mp4
2 S+ M$ V( h3 { │ │ 01_Linear_Regression_Problem_10-08.pdf
4 t! Q1 y8 |0 E" B; @0 r. ^ │ │ 02_Linear_Regression_Algorithm_20-03.mp4
4 @& C: I' j4 Q5 L6 x │ │ 03_Generalization_Issue_20-34.mp4
+ {& j- b: f7 r4 Z9 S" Y │ │ 04_Linear_Regression_for_Binary_Classification_11-23.mp4) r! U- c6 r: ]: z- y% p. i
│ │ 2 g4 \8 x8 V5 |# v, E# g5 Z6 ?
│ ├─10_-_Logistic_Regression
# r# T4 F, E; b& [$ p# Q │ │ 01_Logistic_Regression_Problem_14-33.mp4
( l& B' d5 N/ e. M7 E8 ~ │ │ 01_Logistic_Regression_Problem_14-33.pdf
7 N2 @# L# S+ {( B( v) h$ ? │ │ 02_Logistic_Regression_Error_15-58.mp4
5 N, ?0 X: W+ `8 r- J │ │ 03_Gradient_of_Logistic_Regression_Error_15-38.mp46 S& @1 Y# J9 q" P
│ │ 04_Gradient_Descent_19-18.mp4/ _- |9 q1 G7 M6 _' d# t
│ │ * G, a+ Z7 P3 `7 Z) f' C0 u9 N
│ ├─11_-_Linear_Models_for_Classification
5 A; h0 V! w/ J │ │ 01_Linear_Models_for_Binary_Classification_21-35.mp4
' z$ k! y& }8 Z: k4 g, L9 }9 [ │ │ 01_Linear_Models_for_Binary_Classification_21-35.pdf1 Q" [ O, J: q- K$ ^5 T
│ │ 02_Stochastic_Gradient_Descent_11-39.mp40 I2 f/ g4 r5 |& E( R
│ │ 03_Multiclass_via_Logistic_Regression_14-18.mp4
: C% i8 \7 Y" ~" v9 R* D! [ │ │ 04_Multiclass_via_Binary_Classification_11-35.mp4, F" x4 d* S& d
│ │
& Q0 o/ S: a6 {$ z. d │ ├─12_-_Nonlinear_Transformation& S: }% @* M2 K+ e
│ │ 01_Quadratic_Hypothesis_23-47.mp4! D+ H( ]0 h7 V; ~0 C$ l
│ │ 01_Quadratic_Hypothesis_23-47.pdf B! H4 B6 k3 U3 H4 C
│ │ 02_Nonlinear_Transform_09-52.mp4
]* j: F6 |; ]! B' N │ │ 03_Price_of_Nonlinear_Transform_15-37.mp48 H& X9 x! C6 E! d8 k
│ │ 04_Structured_Hypothesis_Sets_09-36.mp4
3 ^6 Q4 I/ l4 o6 m( C │ │ h! f) }4 F4 n% S, j% j& y
│ ├─13_-_Hazard_of_Overfitting
3 C, }8 u$ g# u; P* d │ │ 01_What_is_Overfitting_10-45.mp4
3 c7 v- r0 B6 V/ @ │ │ 01_What_is_Overfitting_10-45.pdf$ Y5 ?2 p/ h: b& O
│ │ 02_The_Role_of_Noise_and_Data_Size_13-36.mp4
( x& x0 A- B. N; k, |& q+ G( s │ │ 03_Deterministic_Noise_14-07.mp4; R0 x' v6 h4 t) z, ^ l
│ │ 04_Dealing_with_Overfitting_10-49.mp4
/ i, g8 [# y: X0 J$ `/ v │ │ 8 M5 S& Q8 q. q t: s1 N+ O
│ ├─14_-_Regularization9 V/ a4 `7 H% c
│ │ 01_Regularized_Hypothesis_Set_19-16.mp4
5 C9 l3 n$ v+ h, S- ~4 x1 J │ │ 01_Regularized_Hypothesis_Set_19-16.pdf
: k! R9 U2 g' ]8 w7 @ │ │ 02_Weight_Decay_Regularization_24-08.mp46 U7 E1 o3 u, }/ x8 m
│ │ 03_Regularization_and_VC_Theory_08-15.mp4( V# {% `7 l4 v
│ │ 04_General_Regularizers_13-28.mp4; L, a! n& \* Z/ O
│ │
/ a: {1 [1 Y5 w0 Z( f │ ├─15_-_Validation
" y9 J& G/ `- g │ │ 01_Model_Selection_Problem_16-00(437).mp4
+ Z0 y( ^! c' O( p │ │ 01_Model_Selection_Problem_16-00.pdf+ j" O+ M) H: ~# ^* G
│ │ 02_Validation_13-24.mp4
3 Z6 U* T1 @1 @. Q) X │ │ 03_Leave-One-Out_Cross_Validation_16-06.mp4: {- `) p. c) E$ V' N* l
│ │ 04_V-Fold_Cross_Validation_10-41.mp4/ I" D0 [% n$ U0 N/ I1 g& S
│ │ ! s! H) h* q+ p1 q# I6 }) n) z! s. M2 x
│ └─16_-_Three_Learning_Principles
0 V5 V1 g: R* _" v6 F2 b; h) a* c7 |3 d │ 01_Occams_Razor_10-08.mp47 @) V- G0 f& N' C8 F U _8 |
│ 01_Occams_Razor_10-08.pdf5 G+ K' {6 f, o0 ~* H6 {
│ 02_Sampling_Bias_11-50.mp4, f& b# `* i* {& o+ V8 N
│ 03_Data_Snooping_12-28.mp4- J J$ A- u0 A- U% C& x6 ~
│ 04_Power_of_Three_08-49.mp4
& ]& s5 R+ w! e& M6 U2 w │ ; Z* @7 l. r, e) \* v9 U
├─004_机器学习技法
( n* R0 q: Y* {2 C0 Y N! s# Z │ └─004_机器学习技法
' {: i+ B1 D: U5 y: r* J$ ?% F │ └─機器學習技法) A% y! N8 K5 D5 [ u+ C
│ │ 下载说明.txt5 M5 f+ ?/ T' o9 @& g" w7 j+ x1 j: P
│ │ 关注我们.png. a8 |# k0 [( {, }" A
│ │ 攻城狮论坛=网络技术+编程视频.url
2 d4 @7 w! X5 r- |0 m │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
- [5 n, P, k2 A U1 d │ │ & g, v& W8 ~: A4 W U" Y
│ ├─01_Linear_Support_Vector_Machine. D) Z: \; c+ m$ k
│ │ 01_Course_Introduction_4-07.mp4& X) D" h1 u. N y4 |
│ │ 01_Course_Introduction_4-07.pdf2 v5 y9 n. N& j
│ │ 02_Large-Margin_Separating_Hyperplane_14-17.mp4% A- T, o, }$ a/ b
│ │ 03_Standard_Large-Margin_Problem_19-16.mp4
2 k; R6 F6 ~* y# D& D% H5 ~ │ │ 04_Support_Vector_Machine_15-33.mp4- X' \# q% M i! s% p
│ │ 05_Reasons_behind_Large-Margin_Hyperplane_13-31.mp4/ r) F, V3 J+ l* k' G% l5 V
│ │
$ y8 s' h8 G! G# x+ y │ ├─02_Dual_Support_Vector_Machine; H6 O! P: @# Y! B
│ │ 01_Motivation_of_Dual_SVM_15-54.mp4
0 m* P9 \5 g+ k/ z% w │ │ 01_Motivation_of_Dual_SVM_15-54.pdf
; T& ]# \5 z: W! U+ H8 J* X │ │ 02_Lagrange_Dual_SVM_18-50.mp40 z' |8 T( D1 g( k" ^( D# R+ f
│ │ 03_Solving_Dual_SVM_14-19.mp4
. }( P: ~4 C( N4 C: T* x │ │ 04_Messages_behind_Dual_SVM_11-18.mp4
( A$ Z* x* }$ g │ │ # Y& J! }4 H" `6 j. A- g7 k
│ ├─03_Kernel_Support_Vector_Machine2 y1 m8 |$ s) w- C$ m8 V& k& {1 S
│ │ 01_Kernel_Trick_20-23.mp4
% F2 c/ X8 ~1 \, \" s. I: y; { │ │ 01_Kernel_Trick_20-23.pdf
6 z" V" F0 \3 U& D+ r$ E5 o │ │ 02_Polynomial_Kernel_12-16.mp4% C @# ]% S- N0 B; n# O
│ │ 03_Gaussian_Kernel_14-43.mp4
4 m" U. d: Z8 H- X │ │ 04_Comparison_of_Kernels_13-35.mp4" f4 c2 h$ L1 \1 `2 i }. i
│ │ 2 ]" q3 X+ W+ |! P9 ~& ?; T
│ ├─04_Soft-Margin_Support_Vector_Machine b l9 t9 f% }/ J' k
│ │ 01_Motivation_and_Primal_Problem_14-27.mp4* \: Y# m& z; D9 c( A
│ │ 01_Motivation_and_Primal_Problem_14-27.pdf9 G3 |- M: |& n D# y* R, ]
│ │ 02_Dual_Problem_7-38.mp4$ C# @' a! w' V
│ │ 03_Messages_behind_Soft-Margin_SVM_13-44.mp4
5 I3 v8 q: Z8 B# ?! X' \ │ │ 04_Model_Selection_9-57.mp4" l1 s3 n3 X0 l) `. b
│ │
: a; h7 z8 y! E2 D │ ├─05_Kernel_Logistic_Regression, m5 B }# y' c F& l
│ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.mp4
+ |7 n& o! X3 D- T │ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.pdf
% }" \$ Y! `& A3 A @- J │ │ 02_SVM_versus_Logistic_Regression_10-18.mp4; `% f# d* r9 ]2 P' p/ d" C: N
│ │ 03_SVM_for_Soft_Binary_Classification_9-36.mp4
, s5 h! o" G) ~+ c │ │ 04_Kernel_Logistic_Regression_16-22.mp4
$ d/ N( @ v6 d7 m4 b8 _/ D% z │ │ + x8 O/ F( X* L5 U+ b. X' C
│ ├─06_Support_Vector_Regression1 Y4 t5 z) j2 Y9 q- _. z
│ │ 01_Kernel_Ridge_Regression_17-17.mp4
4 v0 [1 N$ E% s/ v+ I0 j/ x │ │ 01_Kernel_Ridge_Regression_17-17.pdf
0 R( h8 v# Q7 a9 s! [' b6 ^/ x │ │ 02_Support_Vector_Regression_Primal_18-44.mp4/ D/ ]. }' R- B2 d$ g: U
│ │ 03_Support_Vector_Regression_Dual_13-05.mp4
! g* X b8 P+ z+ c │ │ 04_Summary_of_Kernel_Models_09-06.mp4
1 j4 H4 J* U4 o! o, ? │ │ & @ r' M; z1 f; t' b$ h! a
│ ├─07_Blending_and_Bagging
$ ~# M* K1 }6 l: w4 D │ │ 01_Motivation_of_Aggregation_18-54.mp4
; _" m& r. \# h$ y; B R │ │ 01_Motivation_of_Aggregation_18-54.pdf! h/ S! ~6 o! H- a. D6 U
│ │ 02_Uniform_Blending_20-31.mp4
3 ~5 q, `: f6 o/ k4 m │ │ 03_Linear_and_Any_Blending_16-48.mp4; p r+ t F2 X, k U! p
│ │ 04_Bagging_Bootstrap_Aggregation_11-48.mp42 ^# S+ P( ~* U8 p# f! E" O
│ │ % i( S; ]) Q8 ]
│ ├─08_Adaptive_Boosting
! z' _* L* L F │ │ 01_Motivation_of_Boosting_12-47.mp4
2 G) g0 w5 \! `1 J" r: w/ T │ │ 01_Motivation_of_Boosting_12-47.pdf
2 J7 [: Q1 m; l' I8 z& a: ^ │ │ 02_Diversity_by_Re-weighting_14-28.mp4
. ]5 e( ]; L/ q │ │ 03_Adaptive_Boosting_Algorithm_13-34.mp4
4 }8 u3 j x/ t6 d1 a) {" g- V │ │ 04_Adaptive_Boosting_in_Action_11-04.mp4! u. B' g/ ^. i
│ │ : W* p. o; F2 ^8 g" i( F
│ ├─09_Decision_Tree
1 ?+ @+ Z( `* I+ Z9 r │ │ 01_Decision_Tree_Hypothesis_17-28.mp4
* ]2 ?& y# \& Q' |6 H3 b$ v6 ? │ │ 01_Decision_Tree_Hypothesis_17-28.pdf
! P/ _% @1 i f2 a7 }" I6 |; C │ │ 02_Decision_Tree_Algorithm_15-20.mp4& i5 _# a* o! O* u5 r- o7 v, ` Y
│ │ 03_Decision_Tree_Heuristics_in_CRT_13-21.mp4& |2 p" \: c1 b) _8 K' g# u
│ │ 04_Decision_Tree_in_Action_8-44.mp4
- q9 l% n0 G c# L" G0 b3 M │ │ . g( n4 N6 O# i- T) ^, M8 } _
│ ├─10_Random_Forest# i. K) @1 b: `# J7 p0 c" _
│ │ 01_Random_Forest_Algorithm_13-06.mp4
8 W6 ]/ z8 a d' s4 h. H+ A │ │ 01_Random_Forest_Algorithm_13-06.pdf
1 t* f0 I3 M( O* G0 h1 v │ │ 02_Out-Of-Bag_Estimate_12-31.mp43 b6 ^; @" I! b
│ │ 03_Feature_Selection_19-27.mp4* D" j* L) K2 d7 }0 J: X
│ │ 04_Random_Forest_in_Action13-28.mp43 \ ]6 d: ^% z' ?1 Z
│ │ ; V7 n& Z& ~8 I
│ ├─11_Gradient_Boosted_Decision_Tree
* u! c( P" P6 r( x! x │ │ 01_Adaptive_Boosted_Decision_Tree_15-05.mp43 [: q8 F3 h9 L2 }
│ │ 01_Adaptive_Boosted_Decision_Tree_15-05.pdf
: g/ D: Y7 G& @" _. g6 ~ │ │ 02_Optimization_View_of_AdaBoost_27-25.mp4: n! X6 _( k% q7 W: h
│ │ 03_Gradient_Boosting_18-20.mp4
/ G8 ]5 H, e3 B7 k$ I0 ^ │ │ 04_Summary_of_Aggregation_Models_11-19.mp4
# E. N3 l( l- O3 D" h( s │ │ ( \" J: e$ k, O# o: W) i
│ ├─12_Neural_Network2 F) ~; [5 u q! k4 ~9 M
│ │ 01_Motivation_20-36.mp4
) o: N+ p) @" e2 _6 x │ │ 01_Motivation_20-36.pdf, R* e% {& @0 [- n; M
│ │ 02_Neural_Network_Hypothesis_18-01.mp4/ |7 U- J. k/ l1 d
│ │ 03_Neural_Network_Learning_22-26.mp47 U6 F' i. Z+ u d: [' e
│ │ 04_Optimization_and_Regularization_17-29.mp4; v0 p% z; z( ^8 F: v& A- V
│ │
6 \. j$ ~+ u a6 w │ ├─13_Deep_Learning6 d& C* u) I1 q3 R7 p
│ │ 01_Deep_Neural_Network_21-30.mp4" l ~/ R* u, I, ^$ W& r
│ │ 01_Deep_Neural_Network_21-30.pdf
$ T Z' ?! @1 F) w │ │ 02_Autoencoder_15-17.mp49 }7 I7 |% w5 {0 w/ T p1 m% t: [5 \
│ │ 03_Denoising_Autoencoder_8-30.mp4
) [ I# R! s1 o! e/ m" r8 A │ │ 04_Principal_Component_Analysis_31-20.mp4
4 o$ P9 L2 m% l) h# h- G4 x! c* X │ │ ' N4 C5 _% S+ Y
│ ├─14_Radial_Basis_Function_Network$ Z) J* Z% D8 V4 d) D+ p3 b) G
│ │ 01_RBF_Network_Hypothesis_12-55.mp4 i5 Q: v) c; ]$ v2 ]' ]/ A' z$ H0 {
│ │ 01_RBF_Network_Hypothesis_12-55.pdf
0 _# }) w; y: D! j" [6 Q8 @ │ │ 02_RBF_Network_Learning_20-08.mp4
$ e7 n( z( v, ?& C │ │ 03_k-Means_Algorithm_16-19.mp48 p8 `1 ?: M0 Z
│ │ 04_k-Means_and_RBF_Network_in_Action_9-46.mp4/ Z2 Q$ N# X- B
│ │ 2 a$ y! |) X' J" q* V
│ ├─15_Matrix_Factorization5 h; a" {* e: s: P& A3 D& h
│ │ 01_Linear_Network_Hypothesis_20-16.mp4; _' @5 M& s, x" @
│ │ 01_Linear_Network_Hypothesis_20-16.pdf
$ V6 a% X0 R. T V9 H5 i │ │ 02_Basic_Matrix_Factorization_16-32.mp48 q$ s. c) {8 j: c5 O% I
│ │ 03_Stochastic_Gradient_Descent_12-22.mp4& G- R6 h, W; d
│ │ 04_Summary_of_Extraction_Models_9-12.mp4
5 s7 m+ b7 {6 d6 \( [$ N' _$ M │ │
; O. g" H' z* n3 J7 U$ f# g │ └─16_Finale {# `! ~5 s0 E* V; E
│ 01_Feature_Exploitation_Techniques_16-11.mp4
1 @# o4 x6 F) l0 m4 m+ G │ 01_Feature_Exploitation_Techniques_16-11.pdf2 L4 S4 D" v* w& K# O5 y6 Z0 c( d
│ 02_Error_Optimization_Techniques_8-40.mp4
8 A0 U- z" M9 u9 l" N │ 03_Overfitting_Elimination_Techniques_6-44.mp4
! u# r4 h% h) v5 l( h1 R │ 04_Machine_Learning_in_Action_12-59.mp4
+ I( Z: D0 _7 X5 z$ e │
{. `7 l* _% o2 ~- k' O) P8 Y2 M' r ├─005_Neural Networks for Machine Learning
* O" ^3 k! S z7 p+ P │ └─005_Neural Networks for Machine Learning
' S) @9 M. t3 } │ │ 下载说明.txt: C6 }# P1 n/ _6 O1 y8 m( Q
│ │ 关注我们.png! {/ K1 G8 U' O" d
│ │ 攻城狮论坛=网络技术+编程视频.url
8 }, _/ B3 j; T2 F │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt' F) _( L% f0 {9 G8 q
│ │ 6 ~2 ]/ }1 r2 l. x6 R4 ]
│ ├─hinton-ml
8 _! H: U E6 w$ G' }3 r9 d │ │ 1.Why do we need machine learning
0 w: ]1 j, q5 R; Z% P │ │ 1.Why do we need machine learning.mp46 ~6 W6 F; G! M7 m% O
│ │ 10.What perceptrons can't do [15 min].mp4. ]/ d. L* g/ {2 s( J: a2 K
│ │ 10.What perceptrons can't do [15 min].srt0 H. n: l8 o+ w, g
│ │ 11.Learning the weights of a linear neuron [12 min].mp4' W& j) f9 x3 T, v. d* C- t: v# P- b
│ │ 11.Learning the weights of a linear neuron [12 min].srt
6 f. p# t7 _8 J/ ? │ │ 12.The error surface for a linear neuron [5 min].mp4& `- j1 H1 q* m& `+ ^
│ │ 12.The error surface for a linear neuron [5 min].srt
: w( F" {: ~7 ~, V │ │ 13.Learning the weights of a logistic output neuron [4 min].mp4/ Y7 ]# N- F! g, Z1 R
│ │ 13.Learning the weights of a logistic output neuron [4 min].srt- G- V) m9 n9 L% R' g3 L/ V
│ │ 14.The backpropagation algorithm [12 min].mp44 D" ^; v/ z1 z: q) I/ g7 V
│ │ 14.The backpropagation algorithm [12 min].srt$ l8 B5 h* ~' `) B( j
│ │ 15.Using the derivatives computed by backpropagation [10 min].mp4
1 h. K a/ Y+ s, b$ f │ │ 15.Using the derivatives computed by backpropagation [10 min].srt
/ C: J/ E& Y0 ^8 a" v9 J; F │ │ 16.Learning to predict the next word [13 min].mp46 [5 S8 D9 A: m; t M' C- [# S- d
│ │ 16.Learning to predict the next word [13 min].srt1 u0 [ Q1 U1 ~. t! g; U
│ │ 17.A brief diversion into cognitive science [4 min].mp4' n8 i: m* h9 v/ R4 ?/ g
│ │ 17.A brief diversion into cognitive science [4 min].srt3 `! o9 O2 N9 e- `" Z7 F3 F1 e7 Q, v
│ │ 19.Neuro-probabilistic language models [8 min].mp4
# ~2 \% x; }$ g8 g Z: W4 t │ │ 19.Neuro-probabilistic language models [8 min].srt
( A# z1 ^, u8 T+ W' s2 L- Z │ │ 2.What are neural networks
) U$ R% k% x4 E/ A0 `* z( d5 e/ | │ │ 2.What are neural networks.mp44 {: x' f o$ F1 c: k0 w# ~
│ │ 20.Ways to deal with the large number of possible outputs [15 min].mp4
9 M0 r- b7 a4 p; u │ │ 20.Ways to deal with the large number of possible outputs [15 min].srt+ b% _3 V# x7 ]$ H+ {
│ │ 21.Why object recognition is difficult [5 min].mp4# H) C. c& n* M O
│ │ 21.Why object recognition is difficult [5 min].srt
4 F, i7 l& A" q. I! D# ~ │ │ 22.Achieving viewpoint invariance [6 min].mp4
5 ?, p, c8 D) Q9 l" l5 x' [ │ │ 22.Achieving viewpoint invariance [6 min].srt
0 d. Z( S: A8 v* P2 s+ j( T5 W │ │ 23.Convolutional nets for digit recognition [16 min].mp4
6 }0 W5 }# ? {7 b! R │ │ 23.Convolutional nets for digit recognition [16 min].srt$ p! k" B7 w( w- d
│ │ 24.Convolutional nets for object recognition [17min].mp4- B& d3 u4 a* C
│ │ 24.Convolutional nets for object recognition [17min].srt
, P1 X- b8 K8 p* e& n9 h0 ] │ │ 25.Overview of mini-batch gradient descent.mp4
3 k/ f: u! ~) k+ k │ │ 25.Overview of mini-batch gradient descent.srt( N a$ a% A/ h) X0 G* [
│ │ 26.A bag of tricks for mini-batch gradient descent.mp4* q, [5 \' o) {* t" k5 ?
│ │ 26.A bag of tricks for mini-batch gradient descent.srt
, ^6 Z9 f2 q1 x! q/ O# }) E4 P │ │ 27.The momentum method.mp4; y- y5 l R# j+ o. t
│ │ 27.The momentum method.srt% z K3 @& ~1 {% Z
│ │ 28.Adaptive learning rates for each connection.mp46 M: v: _) b3 P5 r# P
│ │ 28.Adaptive learning rates for each connection.srt2 u0 |* S, t( `/ `
│ │ 3.Some simple models of neurons [8 min].mp42 [& o4 u( l% y2 s; d
│ │ 3.Some simple models of neurons [8 min].srt
! E6 L- O0 s/ ]9 } │ │ 31.Training RNNs with back propagation.mp49 e: t4 a5 Y% y
│ │ 31.Training RNNs with back propagation.srt
, I, ?) ]- o6 u │ │ 32.A toy example of training an RNN.mp4
3 O2 m, k4 S$ _9 Z4 q+ |* R │ │ 32.A toy example of training an RNN.srt# j; d, H3 L$ |7 g$ A% G/ {
│ │ 33.Why it is difficult to train an RNN.mp41 i" k5 x& R, _
│ │ 33.Why it is difficult to train an RNN.srt/ D8 r9 f4 g0 ?; R+ V$ `
│ │ 34.Long-term Short-term-memory.mp4
4 d5 G6 x( H$ [ │ │ 34.Long-term Short-term-memory.srt
# l0 B& C' S/ _) K4 F │ │ 35.A brief overview of Hessian Free optimization.mp4
& V ?& Z1 ?1 ^9 \1 n │ │ 35.A brief overview of Hessian Free optimization.srt
z0 p7 d9 t. o# s( G# r$ j │ │ 37.Learning to predict the next character using HF [12 mins].mp45 U; m8 d, _- R
│ │ 37.Learning to predict the next character using HF [12 mins].srt
7 R. `4 t* ^( E3 v# F │ │ 38.Echo State Networks [9 min].mp46 l3 X+ D/ A# w8 ^
│ │ 38.Echo State Networks [9 min].srt2 \9 h, V" R3 @ {( K3 _3 N
│ │ 39.Overview of ways to improve generalization [12 min].mp4- I# Y7 F; W6 ^
│ │ 39.Overview of ways to improve generalization [12 min].srt+ k% e! v0 _1 `
│ │ 4.A simple example of learning [6 min].mp46 {( X; d C) M* y/ c" O
│ │ 4.A simple example of learning [6 min].srt
+ A6 S6 l1 l) B# n* }; U/ n& H │ │ 40.Limiting the size of the weights [6 min].mp44 |, t2 h9 m/ n C" X, h
│ │ 40.Limiting the size of the weights [6 min].srt
* S; c; W/ t, Y$ m5 n( A/ o( b │ │ 41.Using noise as a regularizer [7 min].mp48 k/ K" N1 r% L* J5 q
│ │ 41.Using noise as a regularizer [7 min].srt$ V5 r# P9 [& I0 m" L* z& |. J
│ │ 42.Introduction to the full Bayesian approach [12 min].mp4
; O- q8 Z: A7 M/ C( U1 ~- S │ │ 42.Introduction to the full Bayesian approach [12 min].srt
* Y1 N9 ?' r/ T │ │ 43.The Bayesian interpretation of weight decay [11 min].mp4. J* K0 y8 G8 D
│ │ 43.The Bayesian interpretation of weight decay [11 min].srt' W9 W1 }+ H4 d4 w* Z; L
│ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].mp43 _9 c# D# p8 W. }# _9 |
│ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].srt
" w3 J6 ]) A& {5 l- [2 ^; I1 k │ │ 45.Why it helps to combine models [13 min].mp4
5 h3 n" b+ e5 W4 t0 c │ │ 45.Why it helps to combine models [13 min].srt
# x/ |, }) k0 c. C$ R$ [ │ │ 46.Mixtures of Experts [13 min].mp43 Q6 O) C) V7 ^, B& H- u% F2 x+ `8 p
│ │ 46.Mixtures of Experts [13 min].srt
. d. y! |. j9 i" B9 |4 e! u │ │ 47.The idea of full Bayesian learning [7 min].mp4
T% V! }5 R6 e# u3 U+ V) n │ │ 47.The idea of full Bayesian learning [7 min].srt
q8 \; @$ x# I1 H2 c$ R │ │ 48.Making full Bayesian learning practical [7 min].mp40 T# Q3 t" ]# i% O3 n
│ │ 48.Making full Bayesian learning practical [7 min].srt
1 b" M& t+ U' s1 X- t: l" N7 D. e, h9 H │ │ 49.Dropout [9 min].mp4' `5 M, B3 a( x4 |. B1 M. o
│ │ 49.Dropout [9 min].srt. a1 G, S& ~ V% e
│ │ 5.Three types of learning [8 min].mp4& I4 f6 o. D- d) O! P2 }( Z
│ │ 5.Three types of learning [8 min].srt
( i- z+ c* h3 K+ K( H: Y! e │ │ 50.Hopfield Nets [13 min].mp4; C9 Q% ~/ e# k7 x; q
│ │ 50.Hopfield Nets [13 min].srt
( v0 M, l/ p- x │ │ 51.Dealing with spurious minima [11 min].mp4
! s& C1 w/ @6 ]. w. ]5 B3 f │ │ 51.Dealing with spurious minima [11 min].srt
7 \% |& I; ^ L+ j7 h3 g O7 l: |) { │ │ 52.Hopfield nets with hidden units [10 min].mp4
2 n- O* a. D: c1 ?! E │ │ 52.Hopfield nets with hidden units [10 min].srt Q4 O x2 h |- z
│ │ 53.Using stochastic units to improv search [11 min].mp4
3 \! m4 M. N( C+ a; P$ n9 f │ │ 53.Using stochastic units to improv search [11 min].srt5 B0 ~; @9 b3 G" F3 z: f
│ │ 54.How a Boltzmann machine models data [12 min].mp4- D( H+ _; y5 }: V' h
│ │ 54.How a Boltzmann machine models data [12 min].srt l* s, R6 \. k# b
│ │ 55.Boltzmann machine learning [12 min].mp4
' q6 n( H# x6 d7 `2 f │ │ 55.Boltzmann machine learning [12 min].srt/ d: d# M2 [# P. y
│ │ 57.Restricted Boltzmann Machines [11 min].mp4
/ l% W% p$ |7 ]9 \+ L │ │ 57.Restricted Boltzmann Machines [11 min].srt# }1 V: R/ @9 b
│ │ 58.An example of RBM learning [7 mins].mp4 `1 z E ^: E# W$ r' v
│ │ 58.An example of RBM learning [7 mins].srt
: a6 m3 W$ N; B/ @" p0 ^ │ │ 59.RBMs for collaborative filtering [8 mins].mp4
G% W2 Z: k( c% H$ z │ │ 59.RBMs for collaborative filtering [8 mins].srt, g1 {5 K! ?' O2 h
│ │ 6.Types of neural network architectures [7 min].mp46 W4 n- l5 Q! y6 G! W8 |( S: W
│ │ 6.Types of neural network architectures [7 min].srt, O. J* y6 ]9 L6 c) D
│ │ 60.The ups and downs of back propagation [10 min].mp4- U: [1 C4 o& y
│ │ 60.The ups and downs of back propagation [10 min].srt
# d& _9 I' F8 f" D: S) w/ \ │ │ 61.Belief Nets [13 min].mp4
3 c! K# o; l0 e# w7 [ │ │ 61.Belief Nets [13 min].srt
7 \. C6 R! g) Y2 I& L/ a3 G; g │ │ 62.Learning sigmoid belief nets [12 min].mp4
! z+ g' M. _6 I' J/ o7 e0 r │ │ 62.Learning sigmoid belief nets [12 min].srt& ?9 _& _4 t% g2 K+ F
│ │ 63.The wake-sleep algorithm [13 min].mp4) p9 Q# E. \/ ]' X' Y9 j0 K% W
│ │ 63.The wake-sleep algorithm [13 min].srt
6 V2 M' I5 o+ Y3 S7 q │ │ 64.Learning layers of features by stacking RBMs [17 min].mp4* x, [! ?( V" w' `5 Q! P
│ │ 64.Learning layers of features by stacking RBMs [17 min].srt
" e/ |, Q: g0 F! `2 v, w4 G │ │ 65.Discriminative learning for DBNs [9 mins].mp4+ K8 W8 P+ K$ Q/ f" @2 C6 d+ q3 `
│ │ 65.Discriminative learning for DBNs [9 mins].srt
3 c. d; P& F" H K │ │ 66(1).What happens during discriminative fine-tuning
' k/ o$ i4 h' p3 X. b( Z# {/ m │ │ 66.What happens during discriminative fine-tuning% n* h/ T4 s: t' C. ? H
│ │ 67.Modeling real-valued data with an RBM [10 mins].mp4
! q- f% j$ z/ R │ │ 67.Modeling real-valued data with an RBM [10 mins].srt+ H) s3 d) V+ I$ I1 P& s! ~
│ │ 69.From PCA to autoencoders [5 mins].mp45 H: K# E W% X5 F
│ │ 69.From PCA to autoencoders [5 mins].srt
N6 Z9 r$ J; P! t( e$ ^; }7 j │ │ 70.Deep auto encoders [4 mins].mp44 B$ y' g. J# G3 Z/ ]
│ │ 70.Deep auto encoders [4 mins].srt
6 o" v- k0 ]7 g& x" T │ │ 71.Deep auto encoders for document retrieval [8 mins].mp47 I( \6 ~3 g/ F6 P0 N; x9 X
│ │ 71.Deep auto encoders for document retrieval [8 mins].srt: S! @9 K% B/ _1 Z
│ │ 72.Semantic Hashing [9 mins].mp4
$ a0 }; Y/ @* L, k" u │ │ 72.Semantic Hashing [9 mins].srt
1 P4 ?! s) u7 N$ }0 v │ │ 73.Learning binary codes for image retrieval [9 mins].mp47 j C2 \, r6 x- \
│ │ 73.Learning binary codes for image retrieval [9 mins].srt
! X! q, P ~" H% e! E4 {0 q │ │ 74.Shallow autoencoders for pre-training [7 mins].mp4
5 Y7 k: ?- y# z7 K │ │ 74.Shallow autoencoders for pre-training [7 mins].srt
) z# I9 B2 {$ y# X& `9 o5 v │ │ 8.A geometrical view of perceptrons [6 min].mp4
+ }8 l5 w! J1 a9 ]$ [- J │ │ 8.A geometrical view of perceptrons [6 min].srt
0 A$ [' [- H! s/ V │ │ 9.Why the learning works [5 min].mp4
1 X j6 ?( q& w: C8 w7 y! H │ │ 9.Why the learning works [5 min].srt
- r9 C7 X. @% e$ Q │ │ ' _7 U9 Q! u* o9 d
│ └─neuralnets-2012-001! O ?% y: U* E2 |+ ^
│ ├─01_Lecture1. j5 S* v" T! { s+ S0 G
│ │ 01_Why_do_we_need_machine_learning_13_min.mp4+ Y$ L% s, P' l6 h
│ │ 01_Why_do_we_need_machine_learning_13_min.pdf, n: p% u+ b- R6 n
│ │ 01_Why_do_we_need_machine_learning_13_min.pptx
# f/ H, q9 c5 G+ T │ │ 01_Why_do_we_need_machine_learning_13_min.srt
. ^8 A, Q& U) F# z6 U │ │ 01_Why_do_we_need_machine_learning_13_min.txt
8 F& H) c' U1 d │ │ 02_What_are_neural_networks_8_min.mp4/ n' {0 w6 I1 i0 ]8 x1 P A' x$ L0 c8 ?
│ │ 02_What_are_neural_networks_8_min.srt3 F0 Y$ n3 |. i- h9 {0 C4 M- x
│ │ 02_What_are_neural_networks_8_min.txt
, W3 f% O: _ z │ │ 03_Some_simple_models_of_neurons_8_min.mp4
4 W0 H3 D% J O T. Q- j │ │ 03_Some_simple_models_of_neurons_8_min.srt4 a0 c3 f+ k' O' p
│ │ 03_Some_simple_models_of_neurons_8_min.txt7 M9 C/ D4 k! I, L3 P
│ │ 04_A_simple_example_of_learning_6_min.mp47 S; w+ q& e% i3 k5 e" d
│ │ 04_A_simple_example_of_learning_6_min.srt
H5 o; F, d( A │ │ 04_A_simple_example_of_learning_6_min.txt$ A$ Z' P2 ^: S. N) H) T% Y
│ │ 05_Three_types_of_learning_8_min.mp4/ U+ I2 e3 h' y* }/ {9 I. I8 }
│ │ 05_Three_types_of_learning_8_min.srt G, a+ u& A* C0 n+ d, {- [; ~
│ │ 05_Three_types_of_learning_8_min.txt/ e/ `2 X3 x* C6 F, X, l
│ │ ( C' g, R) X# b' E
│ ├─02_Lecture2
+ ~4 Y3 |5 A+ m# n9 w r8 [, R │ │ 03_A_geometrical_view_of_perceptrons_6_min.mp4
, t& `7 {: o3 [8 W6 ]; W/ J │ │ 03_A_geometrical_view_of_perceptrons_6_min.srt# w2 w9 D5 K# N
│ │ 03_A_geometrical_view_of_perceptrons_6_min.txt
* q7 \% S! @# t |8 Q │ │ 04_Why_the_learning_works_5_min.mp45 k8 v& ]! }9 Y% z
│ │ 04_Why_the_learning_works_5_min.srt+ j" k" H3 w; B- x6 ]
│ │ 04_Why_the_learning_works_5_min.txt
! Z& b0 C9 B% _# g/ z" H │ │ 05_What_perceptrons_cant_do_15_min.mp43 E2 z. B+ G6 N$ _
│ │ 05_What_perceptrons_cant_do_15_min.srt, t5 |) Z7 F2 u0 n
│ │ 05_What_perceptrons_cant_do_15_min.txt- `2 ~; d4 k& ^$ X& B
│ │ # D1 ~+ ~- F8 X9 W5 b
│ ├─03_Lecture37 @/ a" j/ r) ?9 W
│ │ 04_The_backpropagation_algorithm_12_min.mp4
2 o7 P0 A, g/ J │ │ 04_The_backpropagation_algorithm_12_min.pdf# ?5 F( ]; [! H L4 G
│ │ 04_The_backpropagation_algorithm_12_min.srt4 N% G8 s9 k& t9 V" Y/ p8 t) Q1 b
│ │ 04_The_backpropagation_algorithm_12_min.txt; T; @6 N, Q* k$ Z% B
│ │ 0 {# Y' ?; I+ n _6 x6 |0 |. A( i
│ ├─04_Lecture4
$ R: ]2 s' E2 g: J │ │ 01_Learning_to_predict_the_next_word_13_min.mp4% }5 ?- h; C8 ~# P0 r# ~2 d2 ^' G
│ │ 01_Learning_to_predict_the_next_word_13_min.pdf
1 q/ `0 l1 p: z7 M {( ^3 V$ ] │ │ 01_Learning_to_predict_the_next_word_13_min.pptx9 [: m* g; ?' \; Z' n7 B' }
│ │ 01_Learning_to_predict_the_next_word_13_min.srt
2 ]! Z9 W- b( Y1 w8 l │ │ 01_Learning_to_predict_the_next_word_13_min.txt
9 c. n; J# f. U! [: D6 | │ │ 04_Neuro-probabilistic_language_models_8_min.mp4% i% w J/ Y* h8 N2 |
│ │ 04_Neuro-probabilistic_language_models_8_min.pdf& _+ z+ q$ f; e
│ │ 04_Neuro-probabilistic_language_models_8_min.srt# w" l( D- k/ j7 O$ m
│ │ 04_Neuro-probabilistic_language_models_8_min.txt+ R& K! j* R* i4 y+ E
│ │ 8 P& o9 ^/ @! R1 _9 _7 k/ H
│ ├─05_Lecture5
: |3 _: }. _' J. f9 f; P │ │ 01_Why_object_recognition_is_difficult_5_min.mp4
9 F7 { @) z+ u+ i │ │ 01_Why_object_recognition_is_difficult_5_min.pdf
7 _ f' t4 `5 m L b │ │ 01_Why_object_recognition_is_difficult_5_min.srt
2 J: y: B$ y" W+ r2 Z9 P8 i/ [ │ │ 01_Why_object_recognition_is_difficult_5_min.txt
5 Z$ x$ J4 p2 t+ c8 v6 a8 H │ │ 02_Achieving_viewpoint_invariance_6_min.mp4 y3 W. Z) k0 {) r$ p- S( z$ R: r
│ │ 02_Achieving_viewpoint_invariance_6_min.srt/ _! a* ]. {1 m: z! v
│ │ 02_Achieving_viewpoint_invariance_6_min.txt
. w% p3 o) A& n8 k" J( W# O+ g! | │ │
# N5 ~9 T& z) X! K+ @ │ ├─06_Lecture6& f: n l: Q: p/ ^% `) _8 J# ~
│ │ 01_Overview_of_mini-batch_gradient_descent.mp4
9 E0 Q1 T* o6 x! r- P" i0 C4 `% c9 d │ │ 01_Overview_of_mini-batch_gradient_descent.pdf
/ F) e, H8 Q3 y9 D0 H │ │ 01_Overview_of_mini-batch_gradient_descent.pptx
2 J- S! E, C( a$ _$ A7 Y/ }; o7 k. {7 l │ │ 01_Overview_of_mini-batch_gradient_descent.srt
* a! l+ |8 |4 J │ │ 01_Overview_of_mini-batch_gradient_descent.txt
* n1 a9 s/ H$ k0 r │ │ 03_The_momentum_method.mp44 @, o% a: x9 ~6 ]% Q0 y1 J$ ?
│ │ 03_The_momentum_method.srt% E" H+ L0 O a7 q3 y
│ │ 03_The_momentum_method.txt+ [6 b2 w9 \* z4 L+ s [; g# ]
│ │ + ~8 X& P1 a3 k( C* [* Z
│ ├─07_Lecture7+ b% S3 I; C, H/ x
│ │ 01_Modeling_sequences-_A_brief_overview.mp4/ f2 u0 L4 r) Y, c7 X- V B# P
│ │ 01_Modeling_sequences-_A_brief_overview.pdf0 a& X- k9 L6 `( R; ]3 k
│ │ 01_Modeling_sequences-_A_brief_overview.pptx
2 O/ `- h% g% R2 M │ │ 01_Modeling_sequences-_A_brief_overview.srt
9 c z2 s- x4 x1 }( j; M J1 k# H2 f7 Y │ │ 01_Modeling_sequences-_A_brief_overview.txt, \, P5 t' j |2 G+ b9 \
│ │ 02_Training_RNNs_with_back_propagation.mp49 \- h g4 c0 H+ D
│ │ 02_Training_RNNs_with_back_propagation.srt6 \8 t3 Y: O2 B
│ │ 02_Training_RNNs_with_back_propagation.txt
5 b) J: Y; Q# ]! ?6 h1 \5 @; X │ │ 03_A_toy_example_of_training_an_RNN.mp4" U8 S( f3 S$ u# H, |& A
│ │ 03_A_toy_example_of_training_an_RNN.srt% X# _3 N; @2 y6 q6 U% O
│ │ 03_A_toy_example_of_training_an_RNN.txt
% n$ o. n' k. n1 Z. y │ │ 04_Why_it_is_difficult_to_train_an_RNN.mp4- q) x9 t1 A, T' f! Y4 |, q
│ │ 04_Why_it_is_difficult_to_train_an_RNN.srt
1 R& S) c1 i# A q │ │ 04_Why_it_is_difficult_to_train_an_RNN.txt, ~) }8 y; z8 F; x6 d) b o
│ │ 05_Long-term_Short-term-memory.mp4) ^% O5 Q' u* k5 k- p) O. ]
│ │ 05_Long-term_Short-term-memory.pdf1 q2 ?/ e: h- C: S
│ │ 05_Long-term_Short-term-memory.srt. k0 o$ f7 t1 L% W" M
│ │ 05_Long-term_Short-term-memory.txt" m: N, ?6 M8 s' C0 y
│ │ D- l* I* ?* O9 I: e' R0 a9 Z
│ ├─08_Lecture8& i* T! {) c4 Y2 y4 T6 t
│ │ 04_Echo_State_Networks_9_min.mp4
( G( g4 \) ?" z X* x, t I0 y │ │ 04_Echo_State_Networks_9_min.srt
) Q6 B; t+ p% F) t! W7 \ │ │ 04_Echo_State_Networks_9_min.txt
& Z8 a* z: W( B │ │
8 q/ j7 `# \, u8 j │ ├─09_Lecture9
' A1 a3 {0 }4 _% _ │ │ 02_Limiting_the_size_of_the_weights_6_min.mp4
( r# \6 h( Q2 M" f& ^; C& K: w │ │ 02_Limiting_the_size_of_the_weights_6_min.srt9 {: V' `. B' M
│ │ 02_Limiting_the_size_of_the_weights_6_min.txt4 h c3 z; L! I4 d+ @ P3 Q0 G
│ │ 03_Using_noise_as_a_regularizer_7_min.mp4/ x% {3 B2 ^# e" P
│ │ 03_Using_noise_as_a_regularizer_7_min.srt2 h8 q% Y6 ^ e3 G3 m
│ │ 03_Using_noise_as_a_regularizer_7_min.txt
9 z) A- N! i. d G" | │ │ . \& x) | y# d+ ]. k7 t
│ ├─10_Lecture10
, ]3 ?3 K- D- U( n │ │ 01_Why_it_helps_to_combine_models_13_min.mp4
) B' o3 T; X* [4 X: @ │ │ 01_Why_it_helps_to_combine_models_13_min.pdf
2 u4 \3 t% u: |1 J │ │ 01_Why_it_helps_to_combine_models_13_min.pptx+ K" S' Q+ X. Z6 c
│ │ 01_Why_it_helps_to_combine_models_13_min.srt. E# h8 J% F) V! y7 m% B
│ │ 01_Why_it_helps_to_combine_models_13_min.txt
+ d; |5 z) [7 F: s# l d' Q │ │ 02_Mixtures_of_Experts_13_min.mp4- V1 T7 ?$ y' X9 e
│ │ 02_Mixtures_of_Experts_13_min.pdf
1 V( g. i( o1 z9 U │ │ 02_Mixtures_of_Experts_13_min.srt
( S; z# d& {$ M) C1 A+ T6 n │ │ 02_Mixtures_of_Experts_13_min.txt
7 `) E" \3 W0 _4 u │ │ 03_The_idea_of_full_Bayesian_learning_7_min.mp4
: }( D7 H u! W │ │ 03_The_idea_of_full_Bayesian_learning_7_min.srt, p; a0 }/ t( C6 y/ z' b
│ │ 03_The_idea_of_full_Bayesian_learning_7_min.txt
0 s) ?8 f% Y2 [; S5 d │ │ 05_Dropout_9_min.mp4
+ D, N1 Q# Z8 n │ │ 05_Dropout_9_min.pdf
5 a7 \* U, a4 `$ w │ │ 05_Dropout_9_min.srt
! j# _% n, G0 B2 S( K: h! \ │ │ 05_Dropout_9_min.txt: p% m K' m# k
│ │
) @6 M9 e4 Q1 Y, t, V( A/ q) ]* j │ ├─11_Lecture11/ M, y* A, k0 C. M& m
│ │ 01_Hopfield_Nets_13_min.mp42 [* ?0 r- x6 v- S
│ │ 01_Hopfield_Nets_13_min.pdf
: K$ H5 Z5 O1 p& W4 T │ │ 01_Hopfield_Nets_13_min.pptx* Z. h* @! u/ t n! d% B9 X
│ │ 01_Hopfield_Nets_13_min.srt
: K! T3 X3 z# E/ R │ │ 01_Hopfield_Nets_13_min.txt
/ a5 S5 u3 g3 B. u │ │ 02_Dealing_with_spurious_minima_11_min.mp4
+ g- j) J5 p+ j! B9 R │ │ 02_Dealing_with_spurious_minima_11_min.srt
# \# Y u: F5 L │ │ 02_Dealing_with_spurious_minima_11_min.txt6 |5 t8 {% J( P6 V1 g
│ │ 03_Hopfield_nets_with_hidden_units_10_min.mp4
0 S6 v0 H' e' E5 n1 X4 [: | │ │ 03_Hopfield_nets_with_hidden_units_10_min.srt# l# v6 b+ i. g( Y/ g
│ │ 03_Hopfield_nets_with_hidden_units_10_min.txt
1 I% m9 v$ H; r" | │ │ ! y8 F9 E, d- P3 S* r. y- n
│ ├─12_Lecture12) q& q4 k( }& S( Q6 r; W
│ │ 01_Boltzmann_machine_learning_12_min.mp4
, ]1 A8 i8 V3 X! Y0 B& w+ m │ │ 01_Boltzmann_machine_learning_12_min.pdf
" f, ?# I9 ]7 Z0 ?$ w9 ? │ │ 01_Boltzmann_machine_learning_12_min.pptx# M: o4 o; r/ t7 ^. o) N; f( W
│ │ 01_Boltzmann_machine_learning_12_min.srt
9 V; @& ?. x# k2 f: D" X │ │ 01_Boltzmann_machine_learning_12_min.txt
' d' A5 ~, e( {+ } │ │ 03_Restricted_Boltzmann_Machines_11_min.mp4
2 U6 P0 ~9 e0 Q6 I$ r │ │ 03_Restricted_Boltzmann_Machines_11_min.srt U4 J2 K2 _4 c) q
│ │ 03_Restricted_Boltzmann_Machines_11_min.txt
) c+ o% S4 Q6 m7 | │ │ 04_An_example_of_RBM_learning_7_mins.mp4
6 U, G+ k5 z# J7 U/ s │ │ 04_An_example_of_RBM_learning_7_mins.srt2 a! K* v+ n3 Z& E3 Z/ p& K
│ │ 04_An_example_of_RBM_learning_7_mins.txt( s( A' ]# _6 u) |9 L; G
│ │ 05_RBMs_for_collaborative_filtering_8_mins.mp4; \! D+ [+ L% F9 i; M1 q
│ │ 05_RBMs_for_collaborative_filtering_8_mins.srt0 [* y2 A% O) P5 o6 N2 l
│ │ 05_RBMs_for_collaborative_filtering_8_mins.txt
# x+ O! h* S3 j8 G+ z │ │ . Z! r. A1 @1 K- ~
│ ├─13_Lecture138 F* y) S; c5 {% Q
│ │ 02_Belief_Nets_13_min.mp43 a& m( b0 m' A- `' z0 _
│ │ 02_Belief_Nets_13_min.srt7 Q$ d) K) C" C
│ │ 02_Belief_Nets_13_min.txt! ]6 p0 \" U+ e* X
│ │ 03_Learning_sigmoid_belief_nets_12_min.mp4' e% J2 c) u% v8 I6 k& A, }4 i5 J
│ │ 03_Learning_sigmoid_belief_nets_12_min.pdf6 Z; m; F `/ ]" E/ F6 q: i
│ │ 03_Learning_sigmoid_belief_nets_12_min.srt
( }% Z& d+ @7 p │ │ 03_Learning_sigmoid_belief_nets_12_min.txt* P L @0 m" [& p8 K+ j: V
│ │ 04_The_wake-sleep_algorithm_13_min.mp46 E: B5 j1 p7 m- ]
│ │ 04_The_wake-sleep_algorithm_13_min.pdf/ t0 A* I- O- T5 N$ X3 ^5 s R
│ │ 04_The_wake-sleep_algorithm_13_min.srt& w% w8 O) D6 C
│ │ 04_The_wake-sleep_algorithm_13_min.txt& U9 I/ D e e% O
│ │ 0 m& V4 v! Y( \" R1 d0 V6 A
│ ├─14_Lecture14
6 r; l! g1 v/ c% U5 M0 r: ? │ │ 02_Discriminative_learning_for_DBNs_9_mins.mp4
5 N' H4 E9 K6 ^* M$ t4 n │ │ 02_Discriminative_learning_for_DBNs_9_mins.srt, g9 G/ P# B* B$ ]+ F9 h4 [
│ │ 02_Discriminative_learning_for_DBNs_9_mins.txt! k' M- J( Q1 d2 u: ~
│ │
& e0 ^8 ^* T5 [+ ^9 o │ ├─15_Lecture157 z3 S4 p$ x7 k' ]2 Y5 t4 k
│ │ 01_From_PCA_to_autoencoders_5_mins.mp4
# A/ M5 Z, m3 {6 p' I │ │ 01_From_PCA_to_autoencoders_5_mins.pdf8 g( H0 U2 \; X2 H, R0 F1 g! M
│ │ 01_From_PCA_to_autoencoders_5_mins.pptx7 Z$ y9 a+ F; O4 J/ ~, E. L
│ │ 01_From_PCA_to_autoencoders_5_mins.srt
$ E6 Y* ^/ x; Q │ │ 01_From_PCA_to_autoencoders_5_mins.txt* T5 M, ~4 `# ]+ K$ J1 y9 Q' C- _
│ │ 02_Deep_auto_encoders_4_mins.mp4
6 @0 H" {4 ?9 H: v; y9 u │ │ 02_Deep_auto_encoders_4_mins.srt4 M) B+ Z- }: p: T
│ │ 02_Deep_auto_encoders_4_mins.txt
0 p" e+ U% A8 J' {6 T1 T/ W │ │ 04_Semantic_Hashing_9_mins.mp4
: Z. ]* \! k( p; C │ │ 04_Semantic_Hashing_9_mins.pdf
: T$ g- z; O0 w ^ │ │ 04_Semantic_Hashing_9_mins.srt
2 Q4 Y/ K+ A; k9 ^ │ │ 04_Semantic_Hashing_9_mins.txt
" _" Y% h- A& X- X │ │
4 ]' l& h) |3 o8 V │ └─16_Lecture168 e0 B( \0 b7 j+ B
│ 04_OPTIONAL-_The_fog_of_progress_3_min.mp4
! A+ q6 S6 g! _ q3 I │ 04_OPTIONAL-_The_fog_of_progress_3_min.pdf- |; z" l7 ?* ^" @' @9 f9 M8 G
│ 04_OPTIONAL-_The_fog_of_progress_3_min.pptx
4 X$ T2 p! h+ C │ 04_OPTIONAL-_The_fog_of_progress_3_min.srt# @& h6 p: h$ N# h0 n1 F* p
│ 04_OPTIONAL-_The_fog_of_progress_3_min.txt( L2 Q6 j8 i* F% D4 M
│
* h. H+ M% p: Q+ v" T9 o. S ├─006_Probabilistic Graphical Models% B; M" N9 D7 t% s6 C
│ └─006_Probabilistic Graphical Models
$ o* R4 B' r0 S0 H │ ├─pgm-003
' i, R# U6 f4 b; |' V │ │ ├─01_Introduction_and_Overview
8 v/ Y* l7 d2 Q+ i │ │ │ 01_Welcome.mp4
& u* l+ c' H6 ^% c; C9 c │ │ │ 01_Welcome.srt2 L O4 V1 i) E9 _; S
│ │ │ 01_Welcome.txt
1 _ K2 E! ?* q& N% o6 r │ │ │ 02_Overview_and_Motivation.mp4" R* I _0 |5 I8 ]* g5 ?
│ │ │ 02_Overview_and_Motivation.srt5 D; F% u+ A6 o: H0 _$ J5 Y# t
│ │ │ 02_Overview_and_Motivation.txt. \; S' F' i6 _! u- x; f% ?
│ │ │ 03_Distributions.mp4/ D2 r! B+ f1 Z
│ │ │ 03_Distributions.srt
) L4 K) `+ b3 \* s+ y0 N3 H │ │ │ 03_Distributions.txt% l8 G1 k; K# A; G
│ │ │ 04_Factors.mp4! n0 a- z0 D7 l8 M; J
│ │ │ 04_Factors.srt
( {, _0 D$ Q8 C3 U4 S │ │ │ 04_Factors.txt! o: q2 X$ ?2 W. q" \
│ │ │ ; [2 n. E# q5 \! q, X
│ │ ├─02_Bayesian_Network_Fundamentals
: y6 j/ s8 W4 S4 k% m9 C │ │ │ 01_Semantics_amp_Factorization.mp4
; X3 o$ h2 F P" d: |) m6 g: b/ J" i: T │ │ │ 01_Semantics_amp_Factorization.srt
2 a4 _# Z* s8 H- H │ │ │ 01_Semantics_amp_Factorization.txt' O! i. D' z7 k0 ?7 p+ c. {5 Q/ }+ e
│ │ │ 02_Reasoning_Patterns.mp4" e6 T0 a& X. ~6 |- B* ]* A2 J' Z
│ │ │ 02_Reasoning_Patterns.srt! }& ^" ^$ ~0 p$ @$ {/ k. c/ F- _
│ │ │ 02_Reasoning_Patterns.txt& B! n& o: [4 @9 Q& }
│ │ │ 03_Flow_of_Probabilistic_Influence.mp4
; E4 ]5 Z* h G9 e1 Z6 A │ │ │ 03_Flow_of_Probabilistic_Influence.srt3 D5 F7 h% m; ?4 P: w; Y7 A; N
│ │ │ 03_Flow_of_Probabilistic_Influence.txt1 b1 L6 ~8 H9 K6 ~" t& b5 k( j
│ │ │ 04_Conditional_Independence.mp4
- M, @" K- X$ G2 J' I9 K! f │ │ │ 04_Conditional_Independence.srt% O! c2 e! H9 x" l
│ │ │ 04_Conditional_Independence.txt( c, l* }# m/ g, G9 s. A
│ │ │ 05_Independencies_in_Bayesian_Networks.mp4
! C1 f9 k4 \8 t# N, N7 _; \ │ │ │ 05_Independencies_in_Bayesian_Networks.srt$ ^! [ p1 H" Q. n- B. [
│ │ │ 05_Independencies_in_Bayesian_Networks.txt- ^4 i+ f! ?/ X1 I) K. G! V) y
│ │ │ 06_Naive_Bayes.mp4
7 X) t) r t' k+ w" z6 N │ │ │ 06_Naive_Bayes.srt
. U6 h# q" Z- [; { │ │ │ 06_Naive_Bayes.txt! q# r. ~5 U' ]2 B# Q$ f* u, ?
│ │ │ 07_Application_-_Medical_Diagnosis.mp4# F" J" g5 o4 _$ E; U5 k# n
│ │ │ 07_Application_-_Medical_Diagnosis.srt
* _! o1 }; v8 H/ s4 {3 m( k8 v" f │ │ │ 07_Application_-_Medical_Diagnosis.txt
7 G( w D& Z. p7 j" @# V; J │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.mp4
& Y( z# J5 c) G/ f l) { │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.srt9 v9 N% Y0 W$ b! J
│ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.txt! [, W, K9 x. _1 q$ E7 t% f( f
│ │ │ & U) S4 `( i0 ]- O1 z/ [
│ │ ├─03_Template_Models: D& c+ Z" H! p1 c# u% N
│ │ │ 01_Overview_of_Template_Models.mp48 ], s% F) J6 _3 b$ O
│ │ │ 01_Overview_of_Template_Models.srt& u% _2 b K$ g! }
│ │ │ 01_Overview_of_Template_Models.txt
0 G+ e' g2 Q5 ], V+ V$ R& R+ e │ │ │ 02_Temporal_Models_-_DBNs.mp4! X& q1 Y; m0 [+ ~* U6 ?; V* e
│ │ │ 02_Temporal_Models_-_DBNs.srt
# }+ `1 u6 I! y z7 k6 L0 ] │ │ │ 02_Temporal_Models_-_DBNs.txt
' q8 H4 Q& _/ N: I9 W, o' R8 N7 ] │ │ │ 03_Temporal_Models_-_HMMs.mp4" w1 [! c& W, Y' O
│ │ │ 03_Temporal_Models_-_HMMs.srt
# p+ O" L" I/ ^" T1 H2 p │ │ │ 03_Temporal_Models_-_HMMs.txt: U$ N" Z g4 @! }7 {8 Y0 ?
│ │ │ 04_Plate_Models.mp4
' \& z7 V4 f. a2 ?% V │ │ │ 04_Plate_Models.srt: ]2 N/ t* C9 O; k. G. i9 q' b: a
│ │ │ 04_Plate_Models.txt
4 N! T# F, x/ q. p/ x2 A( r0 k │ │ │
2 M- K! u" m# Y, |, Q/ M │ │ ├─04_ML-class_Octave_Tutorial6 F4 i2 Z8 v6 p$ m
│ │ │ 01_Basic_Operations.mp4
6 I4 H& K0 d9 P2 w* U │ │ │ 01_Basic_Operations.srt
( N; [& L+ J- S: f9 [8 e) v │ │ │ 01_Basic_Operations.txt
. e( C% l; l. j$ Q: U/ d% v │ │ │ 02_Moving_Data_Around.mp4; w; E0 X" w Z$ r
│ │ │ 02_Moving_Data_Around.srt8 t) W( h9 d0 Q$ _0 {7 Z
│ │ │ 02_Moving_Data_Around.txt
8 n1 o6 O# [$ m; u │ │ │ 03_Computing_On_Data.mp4
- _/ n. k& ` y0 t- y │ │ │ 03_Computing_On_Data.srt
% `& [' N6 f! z3 _9 z' G1 F │ │ │ 03_Computing_On_Data.txt
' L) x8 k1 r+ h) P. C │ │ │ 04_Plotting_Data.mp4
* G2 E0 c+ w% O( E4 A8 O │ │ │ 04_Plotting_Data.srt( _) X' G; @, S7 [
│ │ │ 04_Plotting_Data.txt% ]! X {$ ?. M' ~) _+ c: o
│ │ │ 05_Control_Statements-_for_while_if_statements.mp4
$ V X/ {" b2 p! f │ │ │ 05_Control_Statements-_for_while_if_statements.srt
8 C4 C5 b4 p( n │ │ │ 05_Control_Statements-_for_while_if_statements.txt* v: q# V! w! {. t9 y G
│ │ │ 06_Vectorization.mp4& i% q5 V) \9 m+ Q8 l) k( G' z$ b& C
│ │ │ 06_Vectorization.srt
( c! X, T# E$ U │ │ │ 06_Vectorization.txt+ S, [, v' b9 z6 f
│ │ │ 07_Working_on_and_Submitting_Programming_Exercises.mp4
5 g2 U" _8 e2 h' ^; } │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.srt
% h; L3 w# B4 w9 Q: u c │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.txt
6 H3 H) B/ b; W5 \' \0 K& H+ Q- C │ │ │
& z3 ~" Z. q) E( P# ~9 {0 z! z' ?/ X │ │ ├─05_Structured_CPDs4 g& S3 S C# Z; K
│ │ │ 01_Overview-_Structured_CPDs.mp4; G# B% c4 M+ N4 l# |2 R, Q
│ │ │ 01_Overview-_Structured_CPDs.srt
; u# D5 E" t9 N7 O. r# [: H7 L/ R │ │ │ 01_Overview-_Structured_CPDs.txt' ]. l. n9 q/ U2 n8 P# p2 }) a! K
│ │ │ 02_Tree-Structured_CPDs.mp4 n; A5 F. y3 f5 m
│ │ │ 02_Tree-Structured_CPDs.srt
) V: ^7 ?" u6 W3 M3 ]* f. H │ │ │ 02_Tree-Structured_CPDs.txt
! N, g3 `8 w1 P' F9 Y │ │ │ 03_Independence_of_Causal_Influence.mp4
0 `# f' z" R+ C9 B3 F, w │ │ │ 03_Independence_of_Causal_Influence.srt& U# M" \/ `& ?7 \% O3 ~. }+ X
│ │ │ 03_Independence_of_Causal_Influence.txt" R7 W5 v4 c6 m, P2 j: n
│ │ │ 04_Continuous_Variables.mp4
4 W k" x. ?1 x% k3 l1 G │ │ │ 04_Continuous_Variables.srt# X4 k. p3 J' ]9 x t: o
│ │ │ 04_Continuous_Variables.txt( V& `- Q4 L1 i* u' K# R
│ │ │ 1 G. v4 C5 o- ~' W( o, {6 z& e/ V% B* p
│ │ ├─06_Markov_Network_Fundamentals& T9 Z5 M! J% U
│ │ │ 01_Pairwise_Markov_Networks.mp47 Q1 Y9 C8 A- {( a
│ │ │ 01_Pairwise_Markov_Networks.srt, u: N: o' E3 J, h
│ │ │ 01_Pairwise_Markov_Networks.txt; m# U. C4 s: T. [
│ │ │ 02_General_Gibbs_Distribution.mp4
* v/ |8 r- M" O! |7 v/ `- I │ │ │ 02_General_Gibbs_Distribution.srt' e: i% N6 ?1 f( t2 Z; r
│ │ │ 02_General_Gibbs_Distribution.txt
1 `8 y3 n+ h: X1 a; B │ │ │ 03_Conditional_Random_Fields.mp4
! Z( J6 k, c" a3 N" }# v- O │ │ │ 03_Conditional_Random_Fields.srt6 d" Q$ D, N# }; `/ M8 [
│ │ │ 03_Conditional_Random_Fields.txt
! X5 ~! k2 ?. ]5 C7 X/ N │ │ │ 04_Independencies_in_Markov_Networks.mp4
# i: L; W' A a& c. e. e- n │ │ │ 04_Independencies_in_Markov_Networks.srt7 a% i3 D% k; u* t, ?- [4 j
│ │ │ 04_Independencies_in_Markov_Networks.txt
% T, @$ s0 a" R3 P) c B" m( N │ │ │ 05_I-maps_and_perfect_maps.mp4( G$ n4 v9 v7 J' ~
│ │ │ 05_I-maps_and_perfect_maps.srt& b% j: K9 o, S/ }" v
│ │ │ 05_I-maps_and_perfect_maps.txt4 X q8 G& }! j
│ │ │ 06_Log-Linear_Models.mp4: w& c+ D5 `; i, v( C% H
│ │ │ 06_Log-Linear_Models.srt/ m$ k" c" \" D8 y M/ Q' j
│ │ │ 06_Log-Linear_Models.txt
( Z# i: U0 O' Z/ D │ │ │ 07_Shared_Features_in_Log-Linear_Models.mp4; v2 \- `% G: J/ a# \. { t
│ │ │ 07_Shared_Features_in_Log-Linear_Models.srt9 B5 w/ P: b0 s0 |9 `
│ │ │ 07_Shared_Features_in_Log-Linear_Models.txt+ G" P S. o8 x" T- e! y9 N
│ │ │ ' M! D# v& `* \
│ │ ├─07_Representation_Wrapup-_Knowledge_Engineering
; I1 V! R+ ^& V% q) W │ │ │ 01_Knowledge_Engineering.mp4
- m$ B- N0 j5 ^9 e │ │ │ 01_Knowledge_Engineering.srt6 l8 j" m3 O: ^4 ^7 E0 X& W0 p
│ │ │ 01_Knowledge_Engineering.txt
* o7 w+ k( ^' a! \9 @# y0 `; [1 @ │ │ │ ) Z! l( e+ y1 A% o: F
│ │ ├─08_Inference-_Variable_Elimination
% V4 g& R0 Y2 U │ │ │ 01_Overview-_Conditional_Probability_Queries.mp4$ N& ^5 s8 G. C, G& f6 T
│ │ │ 01_Overview-_Conditional_Probability_Queries.srt
2 j$ u0 y5 @; ]6 i │ │ │ 01_Overview-_Conditional_Probability_Queries.txt% l( J5 b- s6 ~% P
│ │ │ 02_Overview-_MAP_Inference.mp4
9 F) m z2 ]! S$ e │ │ │ 02_Overview-_MAP_Inference.srt
& E3 S# L9 y; ?" c" y" p- U │ │ │ 02_Overview-_MAP_Inference.txt1 K9 A. m; V& o! o5 r( Q+ }
│ │ │ 03_Variable_Elimination_Algorithm.mp4
) a; G7 L' w- S9 n. j+ F1 ~5 M. ] │ │ │ 03_Variable_Elimination_Algorithm.srt
2 N7 B4 q# v7 C! ~: R5 J │ │ │ 03_Variable_Elimination_Algorithm.txt8 f& c2 n; f$ e
│ │ │ 04_Complexity_of_Variable_Elimination.mp4& V. s# B9 ~& [: s4 [' J/ x" n R
│ │ │ 04_Complexity_of_Variable_Elimination.srt
{+ o/ J6 r, H% ] │ │ │ 04_Complexity_of_Variable_Elimination.txt! |9 K$ G7 I8 Q5 U
│ │ │ 06_Finding_Elimination_Orderings.mp4! k1 t9 Z. S9 m# ^4 D
│ │ │ 06_Finding_Elimination_Orderings.srt
" f4 t R# J, A9 M │ │ │ 06_Finding_Elimination_Orderings.txt1 t" x& [: P% V& x
│ │ │ ( F. v! p; G1 ?: ~2 f( b
│ │ ├─09_Inference-_Belief_Propagation_Part_1& ~' o* ?3 e3 l
│ │ │ 01_Belief_Propagation.mp4
2 R& ]5 B8 x1 f │ │ │ 01_Belief_Propagation.srt% E4 p( P j# [+ ~- h3 b
│ │ │ 01_Belief_Propagation.txt2 R7 m, b* W" |
│ │ │ 02_Properties_of_Cluster_Graphs.mp4* @8 V" B8 a8 I1 h* d; Z
│ │ │ 02_Properties_of_Cluster_Graphs.srt
+ c L$ c7 h- Z z │ │ │ 02_Properties_of_Cluster_Graphs.txt
, `9 H8 z4 x4 z3 F │ │ │
g+ U) \3 Q8 b │ │ ├─10_Inference-_Belief_Propagation_Part_2% l' E# a/ C4 c( [6 x; X- b
│ │ │ 01_Properties_of_Belief_Propagation.mp4
/ e/ \, w1 E& @ F2 k I0 i │ │ │ 01_Properties_of_Belief_Propagation.srt
7 U7 U) j! J% s │ │ │ 01_Properties_of_Belief_Propagation.txt
, u- [* R* m# G6 |9 @ │ │ │ 02_Clique_Tree_Algorithm_-_Correctness.mp4. J4 Q U0 W$ V% K$ E/ ?% m3 ^/ ~9 |9 o
│ │ │ 02_Clique_Tree_Algorithm_-_Correctness.srt
m- Z2 p4 r5 E& B V" [( [ │ │ │ 02_Clique_Tree_Algorithm_-_Correctness.txt; s$ P* O( F: U; s7 j/ W5 p
│ │ │ 03_Clique_Tree_Algorithm_-_Computation.mp4+ M; w* T1 J' ]0 R2 F% M
│ │ │ 03_Clique_Tree_Algorithm_-_Computation.srt
7 s8 n1 r3 |5 V6 D6 D% F) P │ │ │ 03_Clique_Tree_Algorithm_-_Computation.txt9 {/ ^; h8 C* A8 }8 Q9 e+ q; Q
│ │ │ 04_Clique_Trees_and_Independence.mp48 X: `* C1 S' J$ D" t
│ │ │ 04_Clique_Trees_and_Independence.srt- l* \1 O% v. m7 @0 f$ y2 C& y/ u
│ │ │ 04_Clique_Trees_and_Independence.txt
7 c! I. D. ~$ J# R │ │ │ 05_Clique_Trees_and_VE.mp4- p1 k' a; B- `7 g, t8 o. W2 d( Q
│ │ │ 05_Clique_Trees_and_VE.srt4 U Z& |3 N7 L* S0 l6 X7 T
│ │ │ 05_Clique_Trees_and_VE.txt0 E5 o' R8 z- l w( I
│ │ │ 06_BP_In_Practice.mp4- c) X/ w- h0 x' K6 b
│ │ │ 06_BP_In_Practice.srt1 ^5 u$ h9 w8 H4 ~& j& j; L
│ │ │ 06_BP_In_Practice.txt
' a5 P1 ^8 y( O4 Y8 c │ │ │ 07_Loopy_BP_and_Message_Decoding.mp4- c2 v$ D3 A2 g( r; B- e
│ │ │ 07_Loopy_BP_and_Message_Decoding.srt o) o2 w6 w1 Z. G# J
│ │ │ 07_Loopy_BP_and_Message_Decoding.txt) K( X9 m- ^3 b; Q- ~
│ │ │
: v, c, u9 v: c3 m" ~ Y │ │ ├─11_Inference-_MAP_Estimation_Part_1
0 m# S" h Q4 U* g. g. l( ]+ u$ a │ │ │ 01_Max_Sum_Message_Passing.mp41 Q: b1 h m p: `7 x$ e0 |
│ │ │ 01_Max_Sum_Message_Passing.srt5 c4 X5 C/ |- P6 W! _
│ │ │ 01_Max_Sum_Message_Passing.txt( p& c6 v; c3 l x
│ │ │ 02_Finding_a_MAP_Assignment.mp4
% E J# X g! E6 k+ h5 N4 a │ │ │ 02_Finding_a_MAP_Assignment.srt3 R% a; z& @% G5 u: f
│ │ │ 02_Finding_a_MAP_Assignment.txt
0 w5 ], B8 K4 _( ` │ │ │
* y! N; X" ]7 {; S# j, A │ │ ├─12_Inference-_MAP_Estimation_Part_2
3 m* N( D" D& \7 q │ │ │ 01_Tractable_MAP_Problems.mp4
5 a9 S# o' S7 C3 K) F+ b# ` │ │ │ 01_Tractable_MAP_Problems.srt
$ D, j9 H% E0 g, [/ u2 E, }- W │ │ │ 01_Tractable_MAP_Problems.txt2 ` A7 s) g7 N& |4 y5 C! i8 [
│ │ │ 02_Dual_Decomposition_-_Intuition.mp4! e1 V, x4 Z( y. j. M
│ │ │ 02_Dual_Decomposition_-_Intuition.srt
7 V8 d s# P6 p+ Z0 E │ │ │ 02_Dual_Decomposition_-_Intuition.txt+ {, p. M B. y6 H
│ │ │ 03_Dual_Decomposition_-_Algorithm.mp4- f, s& G" | {! e
│ │ │ 03_Dual_Decomposition_-_Algorithm.srt& L: {+ C4 k5 _; q% h7 C! ^+ t
│ │ │ 03_Dual_Decomposition_-_Algorithm.txt P- A* g# f" j# j4 n, H, e
│ │ │
+ t0 d+ d, v, D) [; [+ ?& n2 d0 x │ │ ├─13_Inference-_Sampling_Methods: g( T1 G" f7 K3 F! l2 P- J f1 Y
│ │ │ 01_Simple_Sampling.mp4
( O2 G' V% Z2 ` }0 M& t │ │ │ 01_Simple_Sampling.srt3 Z; ?0 x9 R2 r2 {
│ │ │ 01_Simple_Sampling.txt* a% l& l5 q9 k0 X9 ?' i9 G/ y
│ │ │ 02_Markov_Chain_Monte_Carlo.mp4
, V/ N. H/ a$ P: c3 _. O6 m' i │ │ │ 02_Markov_Chain_Monte_Carlo.srt
8 ^) A; S2 M; ?0 g3 S │ │ │ 02_Markov_Chain_Monte_Carlo.txt
, {2 e! ?& g$ g# T: M │ │ │ 03_Using_a_Markov_Chain.mp4
0 i' t4 {% B9 l, }3 c: A │ │ │ 03_Using_a_Markov_Chain.srt6 K9 Q; Y0 C v2 Q8 N8 y" Q! ^
│ │ │ 03_Using_a_Markov_Chain.txt
4 B- ~3 d$ O; r/ J) o# F/ W │ │ │ 04_Gibbs_Sampling.mp4
# X' {+ N2 s7 e │ │ │ 04_Gibbs_Sampling.srt7 S" d* J% ^" a, r5 U, y
│ │ │ 04_Gibbs_Sampling.txt
]2 y4 P# h7 F( x: i1 w( e" ? │ │ │ 05_Metropolis_Hastings_Algorithm.mp4
+ Y$ a/ T9 q& e: \ │ │ │ 05_Metropolis_Hastings_Algorithm.srt5 [+ i. H" @5 e1 ?3 q& R8 X
│ │ │ 05_Metropolis_Hastings_Algorithm.txt
( |' O5 N7 a! y: W; Z: Y. P │ │ │ 1 }. i" Q0 ]" X ^# b
│ │ ├─14_Inference-_Temporal_Models_and_Wrap-up
+ F5 I# ]# c" {* d5 @ │ │ │ 01_Inference_in_Temporal_Models.mp46 ] o# t& k% W
│ │ │ 01_Inference_in_Temporal_Models.srt7 i" ?: |/ U8 N7 _' D. w
│ │ │ 01_Inference_in_Temporal_Models.txt+ O2 ^$ p4 I' f; ^7 W2 i
│ │ │ 02_Inference-_Summary.mp4' x( G& [4 v& j m& l+ ^
│ │ │ 02_Inference-_Summary.srt6 C, C. O2 ~- A
│ │ │ 02_Inference-_Summary.txt1 D' A+ e) s" A0 a+ m+ t
│ │ │ 2 S8 m7 C# k7 m6 z1 t1 G! r; g5 i% Q
│ │ ├─15_Decision_Theory
1 S; O: H3 D( Q │ │ │ 01_Maximum_Expected_Utility.mp4
! k+ O* _" v( R$ Y( J │ │ │ 01_Maximum_Expected_Utility.srt
0 @1 T) p! J x8 J; x( ? │ │ │ 01_Maximum_Expected_Utility.txt' D6 w% f8 n5 U
│ │ │ 02_Utility_Functions.mp41 q- k/ W7 q& _! r
│ │ │ 02_Utility_Functions.srt
* l, k' E! m. g% T0 b │ │ │ 02_Utility_Functions.txt
t$ A: J9 _, o/ C& G1 ~ │ │ │ 03_Value_of_Perfect_Information.mp4
9 c1 y" X, v& |( _% H. r# ?* @ │ │ │ 03_Value_of_Perfect_Information.srt
% H' p _5 H' B │ │ │ 03_Value_of_Perfect_Information.txt" r- @9 }) s8 `% t
│ │ │ $ Z' i2 o& Z# B
│ │ ├─16_ML-class_Revision- T3 `! m1 Q% c; R0 E; |
│ │ │ 01_Regularization-_The_Problem_of_Overfitting.mp4/ l$ Z a! f" p6 Y1 W @
│ │ │ 01_Regularization-_The_Problem_of_Overfitting.srt
; Z. i9 }3 ^2 K! t% ` │ │ │ 01_Regularization-_The_Problem_of_Overfitting.txt8 }: `' p2 O! R" i5 P
│ │ │ 02_Regularization-_Cost_Function.mp4
1 `2 x) i- Z5 \) u! [" w4 W │ │ │ 02_Regularization-_Cost_Function.srt* q0 P5 Z0 s/ Y9 w: P9 f
│ │ │ 02_Regularization-_Cost_Function.txt& v5 F2 A6 z8 o* Z
│ │ │ 03_Evaluating_a_Hypothesis.mp40 g8 {% ~5 b+ v* R
│ │ │ 03_Evaluating_a_Hypothesis.srt
. e+ \6 U2 y6 J- G7 @ │ │ │ 03_Evaluating_a_Hypothesis.txt
0 r4 i( b; p/ ^! l- f* e( t& n9 y │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.mp4
7 u1 k- `- o! j │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.srt* K3 H8 u4 a ^
│ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.txt
( p) v Y; W! M* r3 K │ │ │ 05_Diagnosing_Bias_vs_Variance.mp4
9 `8 n# Y& @& b4 b% o# N │ │ │ 05_Diagnosing_Bias_vs_Variance.srt9 [+ Q# Z' Q. E1 G; o4 y8 D7 m' D
│ │ │ 05_Diagnosing_Bias_vs_Variance.txt
{# c4 _! C' K- Z" L! o: _ │ │ │ 06_Regularization_and_Bias_Variance.mp4
' X! x) F! ]+ u E" F/ o( n │ │ │ 06_Regularization_and_Bias_Variance.srt
( {; R) Z- D+ t6 p% h6 W" M, d, k │ │ │ 06_Regularization_and_Bias_Variance.txt
" `) j( @7 _) l; @2 _$ t; A8 b2 t │ │ │ / \) R3 z/ A) B1 _7 j4 P6 ?
│ │ ├─17_Learning-_Overview
! O- F5 G, A- C) A+ r4 l0 b │ │ │ 01_Learning-_Overview.mp43 ^; s1 g+ p4 u! Y$ }3 c' E% J+ y
│ │ │ 01_Learning-_Overview.srt+ `' i8 W V+ K$ H! N
│ │ │ 01_Learning-_Overview.txt
3 R# f) c6 I& U) W5 Y │ │ │ ; ]3 t7 W# _4 ^ O, ?( e$ \" D
│ │ ├─18_Learning-_Parameter_Estimation_in_BNs
) L& s( ]0 X. G) p, ~! c J │ │ │ 01_Maximum_Likelihood_Estimation.mp4
3 T5 w; S# L! ]# r g ^( m/ I* @: ?& M │ │ │ 01_Maximum_Likelihood_Estimation.srt- U* \3 e5 d G' ]* V3 s; p; {8 \6 @
│ │ │ 01_Maximum_Likelihood_Estimation.txt1 ?, T) i* D# q9 F: d4 H
│ │ │ 03_Bayesian_Estimation.mp4
4 s! ?1 z/ t4 n+ ] │ │ │ 03_Bayesian_Estimation.srt
; |$ A: G# @+ C3 i# B │ │ │ 03_Bayesian_Estimation.txt
]$ _: F, ?% ^: e y. U: h │ │ │ 04_Bayesian_Prediction.mp4
, c) r8 U/ s: ^4 k │ │ │ 04_Bayesian_Prediction.srt
2 g3 |/ G* f! T9 J" ]) y" `4 C: H │ │ │ 04_Bayesian_Prediction.txt
1 ^' G" s& c, N, V9 S │ │ │ ' @+ a/ `6 s' m) ^ C- n4 ]
│ │ ├─19_Learning-_Parameter_Estimation_in_MNs" \7 n3 C4 f6 q4 @. n/ t
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.mp44 G( @5 R) _8 z: s+ ^" }
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.srt
o. P+ d! S: Z7 ^- k │ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.txt
: ^- `9 U; s. s) E │ │ │ 2 T! w# @' e9 O5 P& R$ R+ j, l
│ │ ├─20_Structure_Learning( Z) z' e" C. G G9 J5 u# o; j. {+ D2 Z
│ │ │ 01_Structure_Learning_Overview.mp4
" o/ o/ h/ D3 S. Y/ N8 ^+ R │ │ │ 01_Structure_Learning_Overview.srt* ?6 N7 z7 L4 X! [+ W* j t& J; k
│ │ │ 01_Structure_Learning_Overview.txt" [5 L$ e; \8 H: k
│ │ │ 02_Likelihood_Scores.mp4
( n1 W& [+ @$ t" D$ N$ I2 J5 A │ │ │ 02_Likelihood_Scores.srt; k& B9 W# n! L% x i
│ │ │ 02_Likelihood_Scores.txt8 W2 v' R2 v8 g5 d0 ?
│ │ │ 03_BIC_and_Asymptotic_Consistency.mp4' N$ ?8 w2 ^. E# g. ]
│ │ │ 03_BIC_and_Asymptotic_Consistency.srt
/ x8 t* g) m! x5 n; E2 L │ │ │ 03_BIC_and_Asymptotic_Consistency.txt6 Z% D# h" M7 \ [; A4 C8 S( T
│ │ │ 04_Bayesian_Scores.mp4
9 T) p2 D1 X5 J& P5 ]9 L% a- u │ │ │ 04_Bayesian_Scores.srt1 w; q8 Q" u9 h; J
│ │ │ 04_Bayesian_Scores.txt1 p+ ^$ @' W& ?. Q
│ │ │ 05_Learning_Tree_Structured_Networks.mp4
0 V! b v$ z- G+ x! J6 D │ │ │ 05_Learning_Tree_Structured_Networks.srt; `+ R* L; }( K# ^
│ │ │ 05_Learning_Tree_Structured_Networks.txt
" n: o4 U% j, T; L │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.mp4
' l3 g; e. E5 R* W6 T. v │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.srt
( z: ^7 @7 }+ E9 }, G7 ? │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.txt
" G+ a% l" D& J6 p+ R │ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.mp4
/ F. n; z: Z& ?% r+ X# P4 Y' I │ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.srt$ |9 I) j5 B h7 v4 c( K/ n
│ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.txt
) e) ?/ ?1 U) ? │ │ │
" K& h8 b8 `2 ?- }% z8 n │ │ ├─21_Learning_With_Incomplete_Data+ I ^5 t \$ x6 N6 v9 ]
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.mp4/ j+ V. n7 q6 v+ _) M K
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.srt
3 u* M0 a+ z }/ Q! \ │ │ │ 01_Learning_With_Incomplete_Data_-_Overview.txt
2 b+ `4 D8 M; W p% H, @* s1 g │ │ │ 02_Expectation_Maximization_-_Intro.mp4
: ]% J) _# a) B2 [ │ │ │ 02_Expectation_Maximization_-_Intro.srt _. L7 m$ f* R4 r4 X4 `
│ │ │ 02_Expectation_Maximization_-_Intro.txt
" ?2 n, }. k/ J0 S │ │ │ 03_Analysis_of_EM_Algorithm.mp4/ N4 ~6 |* G7 K: U" ]
│ │ │ 03_Analysis_of_EM_Algorithm.srt: ?% ~" m2 D5 ~ U
│ │ │ 03_Analysis_of_EM_Algorithm.txt9 }# b4 J: q, a1 ?6 k
│ │ │ 04_EM_in_Practice.mp43 e1 Z/ Y+ ^( t
│ │ │ 04_EM_in_Practice.srt
( ?' }6 F6 N" N& B8 D& L& T │ │ │ 04_EM_in_Practice.txt
5 ^7 ^; b" T/ k4 N8 y7 v$ Q │ │ │ 05_Latent_Variables.mp42 y/ |* Z+ l* u `/ F3 d
│ │ │ 05_Latent_Variables.srt n& O2 r5 F/ m3 n$ ~
│ │ │ 05_Latent_Variables.txt
$ W) B4 W8 @" h │ │ │ 1 o7 m% ?6 k2 `9 n
│ │ ├─22_Learning-_Wrapup% ~& W$ ?* e" \1 z. s( z6 d
│ │ │ 01_Summary-_Learning.mp4+ N7 K" A/ V; S ?5 T" l/ Q
│ │ │ 01_Summary-_Learning.srt
4 O+ b3 u; [; \- [ │ │ │ 01_Summary-_Learning.txt3 R* X6 T5 S9 O. _( B- [
│ │ │
# q7 t. }, Z% ^" c- i8 h │ │ └─23_Summary, q+ k8 @9 P8 a( i2 r3 {2 e
│ │ 01_Class_Summary.mp4
; U- L2 u8 j# |' }" Y │ │ 01_Class_Summary.srt( Y4 ?2 z3 I* k: s& F
│ │ 01_Class_Summary.txt
. z) |3 W" q( _3 K% C( D2 Z% a │ │
4 L. d8 ]6 b8 e) K │ └─Probabilistic Graphical Models - Stanford2 a% O* {% c! }( ^ s8 O% g
│ 1 - 1 - Welcome! (05-35).mp4' U b7 r+ {" N6 C7 t! h. x) B
│ 1 - 1 - Welcome! (05-35).srt" }: T! p/ ?5 m2 i2 o- q
│ 1 - 2 - Overview and Motivation (19-17).mp4
9 m+ z3 I, c" @0 S; h9 G( { │ 1 - 2 - Overview and Motivation (19-17).srt; l. X; N; K9 A" W, \2 l
│ 1 - 3 - Distributions (04-56).mp4) W4 v; m/ ]: |4 H
│ 1 - 3 - Distributions (04-56).srt
1 R0 Y3 \/ I& R/ W7 b, V7 o │ 1 - 4 - Factors (06-40).mp46 R; M- i3 A# n! Y0 }
│ 1 - 4 - Factors (06-40).srt
3 A6 a( M) {2 \+ t3 C$ l+ a0 I _ │ 10 - 4 - Clique Trees and Independence (15-21).mp4
5 J; \" K& e) j4 W6 t) r: i │ 10 - 4 - Clique Trees and Independence (15-21).srt% U( B, k* L% G8 ~2 s
│ 10 - 5 - Clique Trees and VE (16-17).mp48 X i1 t7 [( [# d$ b
│ 10 - 5 - Clique Trees and VE (16-17).srt
# i# I" T( }* t/ o( } │ 10 - 6 - BP In Practice (15-38).mp4
: I- }" q7 J6 N" f │ 10 - 6 - BP In Practice (15-38).srt
% x1 v4 U, q7 Z! l, k4 G │ 10 - 7 - Loopy BP and Message Decoding (21-42).mp4
: f; `9 w# R+ A/ u7 O │ 10 - 7 - Loopy BP and Message Decoding (21-42).srt
: R) V1 L9 s; ?# d5 T │ 11 - 1 - Max Sum Message Passing (20-27).mp4
7 A9 v3 ]1 D! m# T: H4 r* y3 R │ 11 - 1 - Max Sum Message Passing (20-27).srt' ] M0 G- y2 C# g
│ 11 - 2 - Finding a MAP Assignment (3-57).mp4
! j3 j: d. H* u │ 11 - 2 - Finding a MAP Assignment (3-57).srt: j. ]% v- N2 f8 w3 x
│ 12 - 1 - Tractable MAP Problems (15-04).mp4
9 U& C; X8 d- z3 `( \" c- @ │ 12 - 1 - Tractable MAP Problems (15-04).srt* ~# b5 a5 s5 @$ g$ k$ m0 z
│ 13 - 1 - Simple Sampling (23-37).mp43 b5 `7 X8 G+ e4 c) {; e
│ 13 - 1 - Simple Sampling (23-37).srt: u/ g0 Q# a2 q/ N H I( Z! q
│ 13 - 2 - Markov Chain Monte Carlo (14-18).mp4
4 q+ }: M# D9 {. v │ 13 - 2 - Markov Chain Monte Carlo (14-18).srt* [6 W8 e# ~2 ^$ P) [' D
│ 13 - 3 - Using a Markov Chain (15-27).mp4
$ w+ s1 q+ x3 _& h1 W3 k5 z │ 13 - 3 - Using a Markov Chain (15-27).srt
; W/ H" ]; z, Y6 ^5 u0 y │ 13 - 4 - Gibbs Sampling (19-26).mp4
# j. Y& f0 a6 J │ 13 - 4 - Gibbs Sampling (19-26).srt
4 E- T$ A& k8 ~; D9 c │ 13 - 5 - Metropolis Hastings Algorithm (27-06).mp4
0 A4 F7 W4 \5 l3 T │ 13 - 5 - Metropolis Hastings Algorithm (27-06).srt
% R" M! m! A$ | │ 14 - 1 - Inference in Temporal Models (19-43).mp4& P1 p& n. L# ]" ^& j
│ 14 - 1 - Inference in Temporal Models (19-43).srt
/ j- s6 N8 J; O: n9 ] │ 14 - 2 - Inference- Summary (12-45).mp4
! q: }, _5 c$ D/ t& P- ? │ 14 - 2 - Inference- Summary (12-45).srt2 B* O" u9 ^: }( L
│ 15 - 1 - Maximum Expected Utility (25-57).mp4
. A$ i1 R; H' w │ 15 - 1 - Maximum Expected Utility (25-57).srt
) L$ h' B2 g n+ W │ 15 - 2 - Utility Functions (18-15).mp45 t* R$ N7 m M% t& g' a
│ 15 - 2 - Utility Functions (18-15).srt3 N5 I' Q& z) f5 Z% u9 {
│ 15 - 3 - Value of Perfect Information (17-14).mp4
& g" m& J5 z# d5 U6 T │ 15 - 3 - Value of Perfect Information (17-14).srt8 e6 n2 y1 T+ f9 E
│ 16 - 2 - Regularization- Cost Function (10-10).mp4
* h% g- @$ g1 R) d% f& v, Z │ 16 - 2 - Regularization- Cost Function (10-10).srt
) F2 ]! b; i3 k. T │ 16 - 3 - Evaluating a Hypothesis (07-35).mp43 M1 a% g) P; y/ `, F2 e4 \( _
│ 16 - 3 - Evaluating a Hypothesis (07-35).srt
& m& E5 w6 ?( O( Q' L! ]2 e │ 16 - 5 - Diagnosing Bias vs Variance (07-42).mp4
) T. i; o8 [( Z │ 16 - 5 - Diagnosing Bias vs Variance (07-42).srt
+ o: {" C# F: @% U1 i0 i │ 17 - 1 - Learning- Overview (15-35).mp4
& D4 k8 n' |1 l │ 17 - 1 - Learning- Overview (15-35).srt1 ]5 ?- n4 z, s A1 y1 |' C
│ 18 - 1 - Maximum Likelihood Estimation (14-59).mp4
! r# l& p2 G. A7 V │ 18 - 1 - Maximum Likelihood Estimation (14-59).srt1 p ^1 k% {# o M4 U
│ 18 - 3 - Bayesian Estimation (15-27).mp4+ i: ], U) A h+ A- B, I
│ 18 - 3 - Bayesian Estimation (15-27).srt
7 `- w- g, \. b │ 18 - 4 - Bayesian Prediction (13-40).mp4
# P- j5 `6 {) v: r- q5 d4 o( F │ 18 - 4 - Bayesian Prediction (13-40).srt
6 c% |! n: k) ]! C │ 2 - 1 - Semantics & Factorization (17-20).mp4
$ x; j" G+ p' ^$ Y" W7 C5 S; b │ 2 - 1 - Semantics & Factorization (17-20).srt5 } P/ h( P: v' u( d" I! [
│ 2 - 2 - Reasoning Patterns (09-59).mp4" D. `* v8 z4 D' I1 O
│ 2 - 2 - Reasoning Patterns (09-59).srt
/ c. k) g. m0 d0 Q0 \8 w; ~% q- k │ 2 - 4 - Conditional Independence (12-38).mp4
7 _! ^' w$ `3 X: w │ 2 - 4 - Conditional Independence (12-38).srt
- Z3 Q% `: Y% M: t# X) G c │ 2 - 6 - Naive Bayes (09-52).mp4
2 d3 A7 t. Y3 g, R │ 2 - 6 - Naive Bayes (09-52).srt
- n' H' b" T, p( G │ 20 - 1 - Structure Learning Overview (5-49).mp4. p+ S0 w2 H& q. h1 X3 K; j
│ 20 - 1 - Structure Learning Overview (5-49).srt
7 c8 B( Z5 D+ J0 E9 W) d │ 20 - 2 - Likelihood Scores (16-49).mp4; e( ^- S' T) M& s2 v. k
│ 20 - 2 - Likelihood Scores (16-49).srt
1 [/ M/ f, t( a& K3 w │ 20 - 4 - Bayesian Scores (20-35).mp49 M5 I3 |6 }9 Z- ~7 h7 v( n, W
│ 20 - 4 - Bayesian Scores (20-35).srt
% \* |, U+ D' Y; O3 @' M3 A: m │ 21 - 3 - Analysis of EM Algorithm (11-32).mp4
+ k8 i4 b- l( [5 o1 i │ 21 - 3 - Analysis of EM Algorithm (11-32).srt, j: }4 g8 X% z+ _ n
│ 21 - 4 - EM in Practice (11-17).mp4
2 w% N) P2 w( _7 K6 a" |; ? │ 21 - 4 - EM in Practice (11-17).srt
& m0 a x: e. `" O/ @+ N6 q: v- C │ 21 - 5 - Latent Variables (22-00).mp4- s( C3 \! i5 f
│ 21 - 5 - Latent Variables (22-00).srt* z; d8 K+ w. e: L, a" a ]
│ 22 - 1 - Summary- Learning (20-11).mp4
) m7 t6 J2 T4 ` P2 W │ 22 - 1 - Summary- Learning (20-11).srt3 q- \4 g& D( p/ c% `& Y+ k6 z
│ 23 - 1 - Class Summary (24-38).mp4. E N7 o" ]3 l5 |) {6 b
│ 23 - 1 - Class Summary (24-38).srt, p+ T' f' w7 u
│ 3 - 1 - Overview of Template Models (10-55).mp4# N, M/ g) L6 |$ b \
│ 3 - 1 - Overview of Template Models (10-55).srt
* d* T" A9 I( z |( j │ 3 - 2 - Temporal Models - DBNs (23-02).mp4
, s+ b( Y6 I0 q M& j │ 3 - 2 - Temporal Models - DBNs (23-02).srt
* R) Z4 Q: u; p: v8 K! `! D. x │ 3 - 3 - Temporal Models - HMMs (12-01).mp4 h; w, \# _8 O/ v& b
│ 3 - 3 - Temporal Models - HMMs (12-01).srt
E" o3 F' N+ n/ N+ b$ z │ 3 - 4 - Plate Models (20-08).mp4
' p# S9 P9 d. R- F5 D2 n- h- t# ^ │ 3 - 4 - Plate Models (20-08).srt! H+ n2 W& r9 s( M1 Z
│ 4 - 1 - Basic Operations (13-59).mp48 s+ Q, L. m& }3 U9 H1 H
│ 4 - 1 - Basic Operations (13-59).srt
5 H2 U8 c, E! `+ {1 \ │ 4 - 2 - Moving Data Around (16-07).mp4
5 w0 [; Y4 x3 E# X8 p8 A" m │ 4 - 2 - Moving Data Around (16-07).srt9 a8 T/ F; r; g/ Y
│ 4 - 3 - Computing On Data (13-15).mp4* Q; o9 _/ q1 ~2 T! E
│ 4 - 3 - Computing On Data (13-15).srt9 g% |# A1 M9 n9 h$ u l7 r
│ 4 - 4 - Plotting Data (09-38).mp4
! H4 i. Q! n. N3 p9 G │ 4 - 4 - Plotting Data (09-38).srt L7 q, Z( \5 U" `
│ 4 - 6 - Vectorization (13-48).mp4$ v# I2 f8 B1 B. n! i1 b8 [6 R1 `
│ 4 - 6 - Vectorization (13-48).srt
/ ^+ S& B5 U; [3 C9 F5 g1 C; Z │ 5 - 1 - Overview- Structured CPDs (08-00).mp4
- |, u/ x+ J0 h: _ │ 5 - 1 - Overview- Structured CPDs (08-00).srt
7 x2 ?. N# C; N2 Q J │ 5 - 2 - Tree-Structured CPDs (14-37).mp4
) s, z6 v, e( b4 ` │ 5 - 2 - Tree-Structured CPDs (14-37).srt& ?( e8 }# z# i" q; F
│ 5 - 4 - Continuous Variables (13-25).mp4
$ ]5 i2 m; h: |" P │ 5 - 4 - Continuous Variables (13-25).srt
% p" {1 D6 u- n5 m% p+ K3 Z │ 6 - 1 - Pairwise Markov Networks (10-59).mp4* G% |1 R6 a8 @6 g! o! b
│ 6 - 1 - Pairwise Markov Networks (10-59).srt
( a1 [+ s' ]+ Q │ 6 - 2 - General Gibbs Distribution (15-52).mp49 W) a" \4 J0 ~6 j3 k
│ 6 - 2 - General Gibbs Distribution (15-52).srt) R* N0 x' O7 g c7 Q y, q- i1 T
│ 6 - 3 - Conditional Random Fields (22-22).mp4
- m2 q3 b' O3 s0 v9 T │ 6 - 3 - Conditional Random Fields (22-22).srt
7 X: A" C# c- h& r( B) g │ 6 - 5 - I-maps and perfect maps (20-59).mp4
3 o2 d6 C. a1 I9 x0 ~8 M) B │ 6 - 5 - I-maps and perfect maps (20-59).srt1 N9 }6 D- j5 g
│ 6 - 6 - Log-Linear Models (22-08).mp4* J& v, L5 e/ h* o/ r. t
│ 6 - 6 - Log-Linear Models (22-08).srt- @& }! p+ H/ g
│ 7 - 1 - Knowledge Engineering (23-05).mp44 }) C! F9 Y- E2 ]8 o8 }+ s* C
│ 7 - 1 - Knowledge Engineering (23-05).srt7 e1 q( U: X' Z+ [
│ 8 - 2 - Overview- MAP Inference (09-42).mp41 W7 r3 Q" ?0 e3 ~" j' p
│ 8 - 2 - Overview- MAP Inference (09-42).srt
' [# S3 m/ y( a2 @ │ 8 - 3 - Variable Elimination Algorithm (16-17).mp4) K' V2 P) k6 Q9 M* l0 O) A
│ 8 - 3 - Variable Elimination Algorithm (16-17).srt
# N! [2 e+ B2 H* ^9 e │ 8 - 6 - Finding Elimination Orderings (11-58).mp4/ S z$ U, m; T: L8 }7 P# Q" R$ x$ a
│ 8 - 6 - Finding Elimination Orderings (11-58).srt3 G" y( u! P8 D+ t' s
│ 9 - 1 - Belief Propagation (21-21).mp4& Y+ [+ q8 B0 \1 ~# s
│ 9 - 1 - Belief Propagation (21-21).srt& q) g8 V, X) O: }) Y
│ 9 - 2 - Properties of Cluster Graphs (15-00).mp4
! I! A0 a( w" i0 r2 E0 Q3 d( j │ 9 - 2 - Properties of Cluster Graphs (15-00).srt
; K& s; @$ c+ Y │ PGM-Programming_Assignment_1.zip, U, g8 T% t& Q( w+ B7 t! X7 P
│ PGM-Programming_Assignment_2.zip6 {5 L+ F- }, r# h& R4 M- X+ e
│ PGM-Programming_Assignment_3.zip
4 c+ ^# g) S# l& v │ PGM-Programming_Assignment_4.zip" f% q6 R- g e6 u8 ?. Y
│ PGM-Programming_Assignment_5.zip
+ t& }0 K, u0 H! j4 d1 Q2 a& p, d │ PGM-Programming_Assignment_6.zip) j! E$ |; H. |! | a, v. o6 T2 }0 M
│ PGM-Programming_Assignment_7.zip
4 l; ]- s* M/ `$ T │ PGM-Programming_Assignment_8.zip
4 V; y/ k! R U │ PGM-Programming_Assignment_9.zip7 Z6 n* W9 @( U- ~* M
│ 下载说明.txt
6 T2 I* G3 W) v/ B5 w; G │ 关注我们.png
! ^& v( G* g. d+ z1 V! A( S │ 攻城狮论坛=网络技术+编程视频.url) l; @; H8 A& L- i8 s$ u
│ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt4 z+ X: ^& j! W. x* N, c
│
" x3 W$ L: }$ _- F ├─031_台大概率2 `5 C* J t7 Y, L& b$ z# L
│ └─031_台大概率/ Z5 b. G* u8 m
│ ├─prob-001
2 y6 y: m2 T1 y9 J' i │ │ ├─01_Week_17 v. G4 g `$ M4 N' q
│ │ │ 01_1-0.mp4
s6 Q. A$ g8 j │ │ │ 02_1-1_17-41.mp4
" o& D+ l! ?0 x+ P5 @0 @" ^' i │ │ │ 02_1-1_17-41.pdf( b4 n) Q2 {5 t: y' t+ ^
│ │ │ 02_1-1_17-41_0_.pdf; b# y" D1 |2 D5 f/ i* }# C
│ │ │ 02_1-1_17-41_1_.pdf* G9 ?: i5 ~+ u8 B
│ │ │ 02_1-1_17-41_2_.pdf
. `) m, K5 H7 P5 W8 p2 P, j7 M │ │ │ 03_1-2.a__11-46.mp4
; x- C8 J3 y. l i! O4 t │ │ │ 04_1-2.b__09-40.mp4
/ `0 w+ Q; J/ o* a │ │ │ 05_1-3.a__11-24.mp4
* L; ^, p% @. Z │ │ │ 06_1-3.b__16-36.mp4( K, ?6 L2 A; u5 C% M) R
│ │ │
6 S2 O8 `4 W; F( J. T │ │ ├─02_Week_2$ R/ {9 z( G7 k2 ]. m
│ │ │ 01_2-0_15-32.mp4
0 p) @, v% [7 S/ p │ │ │ 02_2-1.a__16-09.mp4
% ^! g9 D' }7 ^3 S2 u+ E0 D+ t7 _ │ │ │ 02_2-1.a__16-09.pdf! O# i& {) m; B4 {' O
│ │ │ 02_2-1.a__16-09_0_.pdf/ N2 I. B3 w- q7 v) d& x) Z
│ │ │ 02_2-1.a__16-09_1_.pdf
+ K0 v! D& s/ r1 P# G" b# [. K │ │ │ 02_2-1.a__16-09_2_.pdf9 Q# q1 l m! g4 z9 |
│ │ │ 03_2-1.b__16-06.mp4" ?9 t$ f2 F G' M9 N/ e3 m+ s
│ │ │ 04_2-1.c__10-07.mp44 e9 E M1 Q8 r# G
│ │ │ 05_2-2.a__10-41.mp4& @4 R) m( Y( I- X: P: k0 s
│ │ │ 06_2-2.b__12-43.mp45 ?6 D3 g8 h; G1 m
│ │ │ 07_2-2.c__16-02.mp4( ^, V) P& {) S5 j; p
│ │ │
) X9 }% ?- A5 |7 ~ │ │ ├─03_Week_3$ t5 x' L8 ^; H5 I- r: K
│ │ │ 01_3-0_16-46.mp4
& I G( [1 @8 r! x$ O1 c( Q │ │ │ 02_3-1.a___09-12.mp4
n- n+ d5 D5 D0 w/ v5 G4 Y │ │ │ 02_3-1.a___09-12.pdf" f- C3 m, G2 G# ~0 T
│ │ │ 02_3-1.a___09-12_0_.pdf9 s+ I# o2 d/ |( |, y
│ │ │ 02_3-1.a___09-12_1_.pdf0 r/ L; S, }/ O5 i& K, \0 h
│ │ │ 02_3-1.a___09-12_2_.pdf
3 ]% C3 Q% x+ Q7 [) J" D │ │ │ 03_3-1.b__10-35.mp4. i, _% O6 @2 |
│ │ │ 04_3-2_08-47.mp40 B p$ N3 d( V6 k1 \. ]& T
│ │ │ 05_3-3.a__16-57.mp4
, r( W/ \" K; Y4 O2 t% E. m │ │ │ 06_3-3.b__12-58.mp42 E& N N- k$ F/ ~, Y( v
│ │ │
+ W9 f/ a$ a7 p! v, e; ^ │ │ ├─04_Week_4 F5 }" Z6 k6 W) _
│ │ │ 01_4-0_17-33.mp40 x! D! |. p% d1 b, B
│ │ │ 02_4-1.a__13-53.mp4
4 I+ H: U& W5 |, y3 P1 T+ S │ │ │ 02_4-1.a__13-53.pdf3 k$ e1 q! I! U( {% Z @5 Z3 K
│ │ │ 02_4-1.a__13-53_0_.pdf5 ^: ?9 b$ I" X- q7 l
│ │ │ 02_4-1.a__13-53_1_.pdf W6 Y& R% }' r
│ │ │ 02_4-1.a__13-53_2_.pdf/ \; [5 J9 x6 F( A3 X
│ │ │ 03_4-1.b__14-43.mp4
5 M. f+ H5 f% l5 j │ │ │ 04_4-1.c__5-18.mp4% ?( @' D) Y/ T' Y7 F+ a7 K
│ │ │ 05_4-2.a_CDF__9-48.mp4
+ P0 q# o$ s# x+ |2 g+ D │ │ │ 06_4-2.b_CDF__8-59.mp40 R, w. |+ a- ]% S, m1 g; w) C/ X5 e
│ │ │ 07_4-2.c_CDF__9-00.mp4
& \+ C1 q$ j b6 _, q │ │ │ 08_4-3_PMF_11-26.mp44 o8 T4 C8 v, a% {
│ │ │ 09_4-4.a_I__14-41.mp4' G! [2 Y/ P7 H( D- z: t" r) ?# S
│ │ │ 10_4-4.b_I__8-47.mp4
" j! o( j5 C+ C5 d& _. S4 ? │ │ │ $ A4 P3 n( [6 ?& E. E
│ │ ├─05_Week_5- d9 M. e4 ^1 T4 K* |. _' k* p
│ │ │ 01_5-0_14-09.mp4) S k, ^: a' ] W8 Z" z0 q
│ │ │ 02_5-1.a_II__10-36.mp48 w8 A2 d2 g# u4 w4 h$ j! Y" F
│ │ │ 02_5-1.a_II__10-36.pdf5 G5 x$ K7 ~7 F
│ │ │ 02_5-1.a_II__10-36_0_.pdf
0 _% Z; J. q: B I# d; q. y │ │ │ 02_5-1.a_II__10-36_1_.pdf6 H0 i3 {" q) g
│ │ │ 02_5-1.a_II__10-36_2_.pdf& E6 j2 U9 r0 L3 F/ X6 C
│ │ │ 03_5-1.b_II__12-06.mp44 ^/ j- Q. ~7 g, ]( D# g' y) v' P
│ │ │ 04_5-1.c_II__20-28.mp46 ]2 R' a1 t9 p
│ │ │ 05_5-2_PDF_18-56.mp40 w! ^, a5 u$ d, }9 b: W
│ │ │ 06_5-3_I_18-12.mp4" e: [- X/ p$ i5 L! y
│ │ │ 5 N0 P! @9 @0 o" s
│ │ ├─06_Week_65 q# j' ~9 e7 S! `: F; z9 D; s
│ │ │ 01_6-0_10-13.mp4
# `# F: r- N$ X% S7 z/ B │ │ │ 02_6-1.a_II__15-25.mp4
/ h6 B6 S4 Y& c1 R/ x } │ │ │ 02_6-1.a_II__15-25.pdf
3 n+ x3 u8 J/ e' ^5 _5 U( f8 q5 U │ │ │ 02_6-1.a_II__15-25_0_.pdf
6 E4 h4 W+ s) P; c │ │ │ 02_6-1.a_II__15-25_1_.pdf
d' S3 X( ~, Y* J2 ?! q │ │ │ 02_6-1.a_II__15-25_2_.pdf
% {1 J; b" b8 [; r │ │ │ 03_6-1.b_II__16-08.mp4, r8 `0 _ O( i6 h' K# [
│ │ │ 04_6-1.c_II__17-16.mp4
# j( B0 J+ p) C; e( q/ s3 l │ │ │ 05_6-1.d_II__5-40.mp4) {# Z8 ]& k$ z6 d7 ?
│ │ │ 06_6-2.a_I__16-35.mp4+ ? L- R5 d9 V6 X! C Q% z
│ │ │ 07_6-2.b_I__10-41.mp4
/ q1 @- ^" ]3 ?% v: a │ │ │ 08_6-2.c_I__16-44.mp4' R" I$ P, k5 t* I
│ │ │ 09_6-2.d_I__14-30.mp4
% G' j; z- r& S. _ │ │ │
! n( a3 Q: [; D; r( s │ │ ├─07_Week_7
# e* L! y+ R1 m+ ^3 g- K* u │ │ │ 01_7-0.mp4
1 F0 c: j: P( P& }8 d& i% O6 b │ │ │ 02_7-1.a_II__14-31.mp40 s! ` T- D: \" Q+ f
│ │ │ 02_7-1.a_II__14-31.pdf. l& x2 k Q0 ^2 D a- l2 l
│ │ │ 02_7-1.a_II__14-31_0_.pdf* R. y1 k: p: x" u
│ │ │ 02_7-1.a_II__14-31_1_.pdf
" b; v, u: X1 H, N; | │ │ │ 02_7-1.a_II__14-31_2_.pdf! U" I8 v5 q2 B" y, h
│ │ │ 03_7-1.b_II__13-07.mp4
+ m7 ^% D3 t" i4 w │ │ │ 04_7-2.a-___10-35.mp4* S1 T, E/ S, r" t
│ │ │ 05_7-2.b-___08-42.mp4
2 i7 G) ]( ]- J* |5 V) @& }' L6 q! n% q │ │ │ 06_7-3.a-___15-07.mp4
( A' L" h5 W/ L3 U! N1 d. D │ │ │ 07_7-3b-___19-20.mp4
" z3 b- R; a( p* ?( N: W' X │ │ │ ) y- R/ i2 z4 W
│ │ ├─08_Week_8
4 f6 Y' ~& s3 D) s7 { │ │ │ 01_8-0.mp4
5 W9 w$ t9 \7 A# O* i6 T │ │ │ 02_8-1.a__14-36.mp4
% ^/ m5 l7 f- G% S- I' z2 l │ │ │ 02_8-1.a__14-36.pdf, D2 s0 p) z' S+ {5 t: r0 O8 Z
│ │ │ 02_8-1.a__14-36_0_.pdf; m: l6 x4 ~$ t( W) Y% h
│ │ │ 02_8-1.a__14-36_1_.pdf- d" S( h, |) Q, c, E6 x- r
│ │ │ 02_8-1.a__14-36_2_.pdf' y, G, n0 s- c* k/ {+ t4 a5 c
│ │ │ 03_8-1.b__15-05.mp4
* }/ U4 x& E" N. l3 g) U │ │ │ 04_8-1.c__17-00.mp45 v$ [9 l S6 F2 X
│ │ │ 05_8-1.d__11-18.mp4
' M/ S7 x% P( B. b. j2 E │ │ │ 06_8-2_12-32.mp4
- [0 n, S1 d4 u$ m8 f4 G0 F │ │ │ 07_8-3.a__10-06.mp4
- `/ p3 w* M0 Y' {0 x9 }& X; X" p │ │ │ 08_8-3.b__17-27.mp4
( y% t- m" x/ P7 F5 X │ │ │
# l9 M6 a' _; T │ │ └─09_Week_9
, W3 r$ z# t, b) ]% N% ]9 r │ │ 01_9-1.a__11-18.mp4
1 W5 M k7 C+ C% w1 t' ? │ │ 01_9-1.a__11-18.pdf0 r+ g7 i$ z& Q9 }$ P/ i8 v+ z& @
│ │ 01_9-1.a__11-18_0_.pdf' B4 }: y" v6 a1 |1 _: p; N$ ]
│ │ 01_9-1.a__11-18_1_.pdf
4 c5 \7 {, @. i, I) @) U │ │ 01_9-1.a__11-18_2_.pdf
/ E, Q ~ p* o' G │ │ 02_9-1.b__13-49.mp4" f9 W: r. V& d7 t
│ │ 03_9-2.aMGF__10-17.mp4
9 d; G: T* C+ _: f, \! n, B% Q │ │ 04_9-2.bMGF__14-06.mp4
' q, Z N. H3 r- R( t; o8 z │ │ 05_9-2.cMGF__15-53.mp4
+ e; w' C6 a% L1 v │ │ 06_9-3.a__10-35.mp4+ u+ T- W% ]% j- I
│ │ 07_9-3.b__13-01.mp4
# }/ e6 ]1 m, {4 M6 g+ d3 u │ │ 08_9-4.a-__16-45.mp4
( |* V+ b4 P% j% s7 z │ │ 09_9-4.b-__17-19.mp4
7 H7 [$ T. x& f) W2 a& p │ │
% t: ], p. {/ Q' B9 E3 V: m- o │ └─概率-台大+ }$ w, A8 J( N& T8 ^9 ~; ?) g
│ │ 02-8.R
5 R9 I, j) `! ~8 W# ] │ │
/ H5 Q) N9 U2 x: E9 W/ v │ ├─1' O6 _1 o7 `3 j! V/ r9 {
│ │ Benson_Coursera_Week_1_簡.pdf. r! `) k9 D/ ?, _% m: W
│ │ Benson_Coursera_Week_1_繁.pdf
* _8 a) b$ ]4 ~* t │ │ Benson_Coursera_Week_1_繁空.pdf. z: ?: X1 o0 @) s5 [
│ │ 2 - 1 - 1-0：咱們先聊聊，這是門什麼樣的課呢-.mp4, E" S9 K& n m7 N
│ │ 2 - 2 - 1-1：機率概論 (17-41).mp4
3 u. ]- J( P# u( d7 j │ │ 2 - 3 - 1-2.a：集合論 (上) (11-46).mp4- M1 b0 s+ |# e/ i
│ │ 2 - 4 - 1-2.b：集合論 (下) (09-40).mp4
' C8 {1 U6 g4 d. B9 F1 g# z* t │ │ 2 - 5 - 1-3.a：機率名詞 (上) (11-24).mp4
; z' Y3 i) H* X* a+ @ │ │ 2 - 6 - 1-3.b：機率名詞 (下) (16-36).mp4; {, b- o5 O# V, _ U
│ │ 5 h ?" |: t3 }9 H
│ ├─21 O l2 P8 H2 ^3 O; i$ U3 q. o% B" {, ~; G
│ │ Benson_Coursera_Week_2_簡.pdf0 f9 v- n. v6 A# E- D1 S
│ │ Benson_Coursera_Week_2_簡空.pdf8 h7 |- D! [- l. Q- {1 |
│ │ Benson_Coursera_Week_2_繁.pdf
7 E7 v) `9 M) M │ │ Benson_Coursera_Week_2_繁空.pdf
2 s7 w1 S$ J6 d2 o3 z t7 K; L) Z9 S │ │ 3 - 1 - 2-0：咱們聊聊，是學習，還是應付- (15-32).mp4
" e- n7 |- y3 i3 k% Y! G! U& F │ │ 3 - 2 - 2-1.a：機率公理性質 (上) (16-09).mp4. L/ t R m; V1 `8 n
│ │ 3 - 3 - 2-1.b：機率公理性質 (中) (16-06).mp49 h+ ^$ X1 W; b! `
│ │ 3 - 4 - 2-1.c：機率公理性質 (下) (10-07).mp4' Q; |5 u' S" C/ e( W+ p
│ │ 3 - 5 - 2-2.a：條件機率 (上) (10-41).mp49 E/ t v, D3 @
│ │ 3 - 6 - 2-2.b：條件機率 (中) (12-43).mp45 o' J$ G& a0 H# ^4 C5 W' J
│ │ 3 - 7 - 2-2.c：條件機率 (下) (16-02).mp4
" i, M. g2 u7 ~% {- z! f' V# M7 q │ │
* q( \+ ?) |7 y7 Y ^: Z* S │ ├─3# m5 J& Q; S5 m9 W
│ │ Benson_Coursera_Week_3_簡.pdf% [2 Q9 j; g! k r! y" y* z
│ │ Benson_Coursera_Week_3_簡空.pdf; L1 Y% P. E4 A) F% r
│ │ Benson_Coursera_Week_3_繁.pdf+ n: M; ~# ~1 t5 p1 p/ @
│ │ Benson_Coursera_Week_3_繁空.pdf
" H1 d# w! _' e: x" c7 ^' K3 m │ │ 4 - 1 - 3-0：咱們聊聊，常見的錯誤？學習的關鍵？ (16-46).mp4: q; F6 l" t2 c" |* N% p
│ │ 4 - 2 - 3-1.a：機率的獨立性 (上) (09-12).mp4
* y) O7 ^; q; K7 \" p$ G" F1 R U' ~ │ │ 4 - 3 - 3-1.b：機率的獨立性 (下) (10-35).mp4
# ~* F6 l! X+ {/ T7 p7 X │ │ 4 - 4 - 3-2：圖解繁複機率 (08-47).mp4; B5 P* s. Y0 j. B+ b4 a
│ │ 4 - 5 - 3-3.a：數數算機率 (上) (16-57).mp4
; {3 Q6 T& q3 b6 ?$ M& q! J. t │ │ 4 - 6 - 3-3.b：數數算機率 (下) (12-58).mp45 Y+ ?: e, F d5 u6 w+ f
│ │ t, `: e& E3 j. k4 I
│ ├─41 U. ^. B9 @3 y+ e/ V7 U8 G1 |
│ │ Benson_Coursera_Week_4_簡.pdf
( y' f6 E5 T, ?$ M1 i6 w4 L8 E( T │ │ Benson_Coursera_Week_4_簡空.pdf- r. d6 x* {* b9 @/ b' k6 q1 U
│ │ Benson_Coursera_Week_4_繁.pdf8 F: P8 }& \& f
│ │ Benson_Coursera_Week_4_繁空.pdf! @$ x- p: I9 e1 t9 k7 P* x
│ │ 5 - 1 - 4-0：咱們聊聊，如何幫自己面對未來挑戰？ (17-33).mp4' M4 Y- o% v% V3 N# B& y+ B$ ~' Z
│ │ 5 - 10 - 4-4.b：離散機率分佈 I (下) (8-47).mp4' M0 o5 t9 r1 P- O6 D v% R: U
│ │ 5 - 2 - 4-1.a：隨機變數 (上) (13-53).mp4
7 d) t; N1 q3 \6 o │ │ 5 - 3 - 4-1.b：隨機變數 (中) (14-43).mp4
* j, @% \+ [ ~$ a# b │ │ 5 - 4 - 4-1.c：隨機變數 (下) (5-18).mp4
, S/ _9 Q1 W9 U9 T; @0 h │ │ 5 - 5 - 4-2.a：累績分佈函數 CDF (上) (9-48).mp41 ^& N! U/ |; u+ w, G9 B4 ^. m
│ │ 5 - 6 - 4-2.b：累績分佈函數 CDF (中) (8-59).mp4
/ A. T: W. t' ~3 V+ \1 q, c" W$ N │ │ 5 - 7 - 4-2.c：累績分佈函數 CDF (下) (9-00).mp46 {: A y+ e( ]+ v% e, F, {
│ │ 5 - 8 - 4-3：機率質量函數 PMF (11-26).mp4
6 p" A9 t: t9 T# V% `3 l; z$ u │ │ 5 - 9 - 4-4.a：離散機率分佈 I (上) (14-41).mp44 @2 e6 q; f/ ~" {
│ │ ! B& b: p6 A' \: n
│ ├─5
& h' |) \, a7 q( `7 G; o │ │ Benson_Coursera_Week_5_簡.pdf; a+ `% [! s8 I" K8 E
│ │ Benson_Coursera_Week_5_簡空.pdf
* W g9 t# d9 t+ A │ │ Benson_Coursera_Week_5_繁.pdf
' b% _+ o9 \& H8 j │ │ Benson_Coursera_Week_5_繁空.pdf5 r5 K6 {; G9 n
│ │ 6 - 1 - 5-0：咱們聊聊，願你夜夜好眠到天明！ (14-09).mp4
; o: n" u' z" V │ │ 6 - 2 - 5-1.a：離散機率分佈 II (上) (10-36).mp42 H' d- k3 X6 [1 b- p
│ │ 6 - 3 - 5-1.b：離散機率分佈 II (中) (12-06).mp44 E3 I5 x1 P7 b! k
│ │ 6 - 4 - 5-1.c：離散機率分佈 II (下) (20-28).mp4! R- F" O, a4 o, G( d- G
│ │ 6 - 5 - 5-2：機率密度函數 PDF (18-56).mp4
0 Z" I4 N! \' Z7 o! [' j8 o │ │ 6 - 6 - 5-3：連續機率分佈 I (18-12).mp4
# p- e1 a7 [3 N; @0 Z. e5 u. U │ │ , D/ R y z6 F- ?* S; a
│ ├─6
: {& n0 G0 K9 i$ N. ^8 F7 w# f/ S* ? │ │ Benson_Coursera_Week_6_簡.pdf! i0 f# q$ L% N3 ?6 P- j
│ │ Benson_Coursera_Week_6_簡空.pdf: I) v$ l1 }4 H5 H1 m) B9 A
│ │ Benson_Coursera_Week_6_繁.pdf
+ h1 D4 A$ ?7 R │ │ Benson_Coursera_Week_6_繁空.pdf
" s% U, O+ {% P% V# W. |; N) a1 b │ │ 7 - 1 - 6-0：咱們聊聊，成功者的條件是什麼？ (10-13).mp4
' C8 e; C) |3 [ R │ │ 7 - 2 - 6-1.a：連續機率分佈 II (上) (15-25).mp44 l5 d* z7 T' {; s0 h# O& Q
│ │ 7 - 3 - 6-1.b：連續機率分佈 II (中) (16-08).mp4* L3 G9 V% _* }; l
│ │ 7 - 4 - 6-1.c：連續機率分佈 II (下) (17-16).mp4
2 X& N& ~& F& N5 S4 M5 L │ │ 7 - 5 - 6-1.d：連續機率分佈 II (末) (5-40).mp4
8 G4 r$ |" T% |8 V7 }& ] │ │ 7 - 6 - 6-2.a：期望值 I (上) (16-35).mp4
* Z. p3 j7 _, g4 f1 V$ a* F │ │ 7 - 7 - 6-2.b：期望值 I (中) (10-41).mp4
& R6 r9 I; \! q" }* }. `$ d* L │ │ 7 - 8 - 6-2.c：期望值 I (下) (16-44).mp4& |' h) u, R2 M' A) D
│ │ 7 - 9 - 6-2.d：期望值 I (末) (14-30).mp4
% c( I- h' T7 j" H# w7 w1 e. Q) s+ k │ │
+ I7 F4 i& m7 H: {: T* H5 U │ ├─7
9 Z" K9 x& [4 i3 e7 I( k }( s │ │ Benson_Coursera_Week_7_簡.pdf2 ^9 o5 j6 W9 r
│ │ Benson_Coursera_Week_7_簡空.pdf" N R# s1 G: M7 P: F% j5 L
│ │ Benson_Coursera_Week_7_繁.pdf
e" _1 g4 z, f5 D( o# p │ │ Benson_Coursera_Week_7_繁空.pdf5 C# j* v/ @, k, T/ c
│ │ 8 - 1 - 7-0：咱們聊聊，每天都在忙，忙的有用嗎？.mp4
* D5 l% ~' ` Z4 {; W; N │ │ 8 - 2 - 7-1.a：期望值 II (上) (14-31).mp4
1 f- f, E( v' |- N6 u6 I; E x8 V │ │ 8 - 3 - 7-1.b：期望值 II (下) (13-07).mp4$ d1 B5 a' P" h4 J% J
│ │ 8 - 4 - 7-2.a- 隨機變數之函數 (上) (10-35).mp4$ e- u7 p# w& w1 _3 p% o
│ │ 8 - 5 - 7-2.b- 隨機變數之函數 (下) (08-42).mp4
, k. I" w/ Q6 P1 d, b! e+ A │ │ 8 - 6 - 7-3.a- 條件機率分佈與失憶性 (上) (15-07).mp4
; O! w) e4 z. r8 z: r' | │ │ 8 - 7 - 7-3b- 條件機率分佈與失憶性 (下) (19-20).mp4
2 @9 E4 E4 M+ W │ │
7 t0 v' Z, g1 `' [) K │ ├─8
7 Q( b, U! }8 @" t( ?3 l9 `, b2 J' c │ │ Benson_Coursera_Week_8_簡.pdf
/ y2 P9 g5 y, R0 Y$ k │ │ Benson_Coursera_Week_8_簡空.pdf5 O1 o4 I C- h8 V5 j- s
│ │ Benson_Coursera_Week_8_繁.pdf
& ]$ T% E# j3 Z8 j( c9 a │ │ Benson_Coursera_Week_8_繁空.pdf
9 |! e& O! e% x2 ? │ │ 9 - 1 - 8-0：咱們聊聊，如何探索有意義的人生？.mp4
) w" m7 s& ^$ H, ^# Z6 _ │ │ 9 - 2 - 8-1.a：聯合機率分佈 (上) (14-36).mp4) }" R5 Y1 m! O7 }0 D* Q [
│ │ 9 - 3 - 8-1.b：聯合機率分佈 (中) (15-05).mp4
0 u* {8 K3 d4 [. ?, `" a │ │ 9 - 4 - 8-1.c：聯合機率分佈 (下) (17-00).mp4' R5 b$ B [; x3 J+ a
│ │ 9 - 5 - 8-1.d：聯合機率分佈 (末) (11-18).mp4
( I q4 ^0 E7 E; L │ │ 9 - 6 - 8-2：邊際機率分佈 (12-32).mp4
2 ^, [3 M' D+ f( j& |4 [- G │ │ 9 - 7 - 8-3.a：雙變數期望值 (上) (10-06).mp4
9 g. v: C- e: z0 y6 x │ │ 9 - 8 - 8-3.b：雙變數期望值 (下) (17-27).mp4$ A5 j" P$ ~+ `1 {5 X5 F
│ │ " k: R/ p! w9 b
│ └─9
/ P: |& q* S% ?: g5 G, }2 e │ Benson_Coursera_Week_9_簡.pdf
. u3 G5 d& }" {9 B0 ^8 p) k& Y │ Benson_Coursera_Week_9_簡空.pdf
, c" u F$ |0 O7 l& G6 ~ │ Benson_Coursera_Week_9_繁.pdf
- M7 I G& n+ S& Q4 l* V │ Benson_Coursera_Week_9_繁空.pdf: D. ~( a9 ]) @- F" s8 y
│ 10 - 1 - 9-1.a：隨機變數之和 (上) (11-18).mp4; e: {8 t5 } w$ A
│ 10 - 2 - 9-1.b：隨機變數之和 (下) (13-49).mp4
7 t2 |: T" J/ V0 X4 f3 G │ 10 - 3 - 9-2.a：MGF (上) (10-17).mp4
/ _; w3 G( C- u/ O4 k │ 10 - 4 - 9-2.b：MGF (中) (14-06).mp4
5 b% q/ W6 t( Z% O4 ~3 }/ Y; d │ 10 - 5 - 9-2.c：MGF (下) (15-53).mp4( y' z" V. d3 x. `% Z' {- D! L% s
│ 10 - 6 - 9-3.a：多個隨機變數和 (上) (10-35).mp4( G' [0 D3 B% ?8 i8 @: b4 n
│ 10 - 7 - 9-3.b：多個隨機變數和 (下) (13-01).mp4) V K& A8 _2 a, p* I% w
│ 10 - 8 - 9-4.a：中央極限定理-萬佛朝宗 (上) (16-45).mp45 e* A/ f- Q; t
│ 10 - 9 - 9-4.b：中央極限定理-萬佛朝宗 (下) (17-19).mp4
- _8 H+ ~6 K) l* C6 G! w6 v, K' B │
( h# d% C+ `$ K8 R4 ~! D1 W9 | ├─036_Coding the Matrix0 N2 _. i, d2 y& R+ }3 c1 d
│ └─036_Coding the Matrix; X( V- _2 W! G9 ~% s% Z
│ └─matrix-0026 P! o! p: p; I3 X0 y6 b) U
│ │ 下载说明.txt! |' E- v6 Q. Q8 k
│ │ 关注我们.png
0 r" H$ C2 q% I$ o1 X- S4 j8 m │ │ 攻城狮论坛=网络技术+编程视频.url4 T( Q2 A5 O1 F$ E; f0 B" R
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
6 i0 ~& o8 ~/ n, t; M1 @# ? │ │ 0 |0 Z1 ~) c A
│ ├─01_Week_0-_The_Function_and_the_Field. R! P. r' u8 t& k" i L
│ │ 01_Course_Introduction_Part_1__9-53.mp4% C- }) s. Q1 V& M8 ?
│ │ 01_Course_Introduction_Part_1__9-53.srt& L" d2 B; E7 H/ B( ^
│ │ 01_Course_Introduction_Part_1__9-53.txt
9 @/ ^8 u1 |7 A7 Y, Y2 U │ │ 02_Course_Introduction_Part_2__8-49.mp4- e) x! f9 d: b9 ^* a# v4 w
│ │ 02_Course_Introduction_Part_2__8-49.srt3 B7 g4 A' n2 F$ F' ?& H
│ │ 02_Course_Introduction_Part_2__8-49.txt
- Z" F3 |2 d9 V& w+ }, _9 [) M │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.mp4
9 D% H' D" X" S) q5 u │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.pdf
5 t0 ~3 V) y. a │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.srt$ ?; T! W! q3 {9 b5 ~7 O
│ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.txt
$ U3 K1 Z( L1 T. X. ?" m │ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.mp4
, }2 U6 V* e" s G' g! E, O │ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.pdf2 i3 L% U, Q; E5 E- y$ K$ H% s0 Q
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.srt
" O9 Q# U, ^7 f; x% [ │ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.txt
7 o ]) ^( t j* b. Q& J* ?9 U │ │ 05_The_Field-_Playing_with_C_15-19.mp4
- f; R( K# |' h4 K n │ │ 05_The_Field-_Playing_with_C_15-19.pdf& J7 l; F2 O6 }+ Z- t& q
│ │ 05_The_Field-_Playing_with_C_15-19.srt1 R' r- r8 [& B5 [% j3 q
│ │ 05_The_Field-_Playing_with_C_15-19.txt
3 g V5 K4 G9 C& U5 ]8 E7 I/ S7 }0 k │ │ 06_The_Field-_Playing_with_GF2__10-28.mp4
: b8 T5 Y/ F1 x. f9 }7 K7 ?; Y │ │ 06_The_Field-_Playing_with_GF2__10-28.pdf
9 E( k& K; r# k2 r0 A( b- m │ │ 06_The_Field-_Playing_with_GF2__10-28.srt0 D& z9 m0 Q+ Y- L5 Y7 y+ o
│ │ 06_The_Field-_Playing_with_GF2__10-28.txt: }) q1 k% e0 v. H( p
│ │
" w' `" }0 ]: q' Q4 x │ ├─02_Week_1-_The_Vector; a: e" u' P1 d, P
│ │ 01_The_Vector-_What_is_a_vector__8-20.mp4
; C0 `- \7 g" Z& d0 q │ │ 01_The_Vector-_What_is_a_vector__8-20.pdf6 a3 ]$ W5 F( s: z) l& J" h
│ │ 01_The_Vector-_What_is_a_vector__8-20.srt2 O- F% J+ V+ n. L7 G4 ^
│ │ 01_The_Vector-_What_is_a_vector__8-20.txt
& R* X! S# r7 j. x4 A1 i' v │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.mp4' Z. N* i( @( n2 n
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.pdf
1 }& {1 M. v& A4 {% R8 m │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.srt
. i2 e2 t7 ]# {9 |* p& |/ l │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.txt
) Y& m4 J Q/ w- E8 q+ V1 X │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.mp4
) y- h! i) }4 [' t' R1 G( { │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.pdf/ m, \) X" H, k( G- D" D7 A
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.srt
! g/ X$ `! F! ?' t │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.txt
# x* K) X, \& R. I; D! s │ │ 04_The_Vector-_Vectors_over_GF2__9-18.mp48 d1 Z% }6 B" \; k" o% }& n
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.pdf7 d' Z- r9 I$ J% g3 T+ G/ b
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.srt: P' T* |# T m2 I% }+ k1 Q
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.txt/ m9 a9 \: Z% m: x" P
│ │ 05_The_Vector-_Dot-product__8-49.mp4
4 Z; T9 I: |6 c │ │ 05_The_Vector-_Dot-product__8-49.pdf
0 p" k. P( [, u% J8 { │ │ 05_The_Vector-_Dot-product__8-49.srt6 J6 d( `1 \; a5 M
│ │ 05_The_Vector-_Dot-product__8-49.txt
4 z" C& Y1 c1 I" d3 |+ \ │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.mp4* A: _$ e! r* z. e4 y S
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.pdf
7 U3 M0 e9 E b │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.srt9 V P- I; w% O, `6 h3 Z5 {
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.txt; x4 b- d1 Z: p( f
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.mp4: [6 ]5 c$ U9 b h$ t
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.pdf6 J: |4 [6 H* W3 X4 H. R
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.srt
7 O: }1 L7 Y. m4 T3 R1 N │ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.txt
- D' R& e: D) O W$ I' k │ │
& r7 F: M; `" Z& x. x) F1 z │ ├─03_Week_2-_The_Vector_Space
2 \! f+ P0 N9 N2 E3 P5 O- E# B │ │ 01_The_Vector_Space-_Linear_combinations.mp4. c- e" a1 n `: D( f+ ^
│ │ 01_The_Vector_Space-_Linear_combinations.pdf5 F1 x& w" P9 Y( ~3 @
│ │ 01_The_Vector_Space-_Linear_combinations.srt
3 A* ]1 h) O1 ?& A5 b/ A │ │ 01_The_Vector_Space-_Linear_combinations.txt
, @5 Z/ }( h$ w5 E' ?1 t* G │ │ 02_The_Vector_Space-_Span.mp4/ Y- s, N) X l! I( T/ x6 x
│ │ 02_The_Vector_Space-_Span.pdf3 q( {# l7 ?# ^& ^2 @# F, N
│ │ 02_The_Vector_Space-_Span.srt
( J! F( l N8 n- g% ~) _1 U │ │ 02_The_Vector_Space-_Span.txt
: s4 [; T) j! Q6 J+ n9 t │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.mp4
. D1 F h' U& E$ Z7 M- E: O │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.pdf& b- \ w. h$ c6 n# a$ d
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.srt4 q4 H* a; B( Y; n2 z6 X
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.txt
' L4 P3 \3 [: w% r │ │ 04_The_Vector_Space-_Vector_spaces.mp49 v& r9 B- w6 H$ @7 B+ u- n+ o
│ │ 04_The_Vector_Space-_Vector_spaces.pdf
4 u# {2 z1 Y9 ~ │ │ 04_The_Vector_Space-_Vector_spaces.srt( B w$ k7 {& _3 i( @
│ │ 04_The_Vector_Space-_Vector_spaces.txt
* i: Q4 Z/ j+ G │ │ 05_The_Vector_Space-_Checksum_function.mp4, W$ p5 y* i0 k
│ │ 05_The_Vector_Space-_Checksum_function.pdf/ F3 P( u$ E$ a# {* }5 b! ^
│ │ 05_The_Vector_Space-_Checksum_function.srt
- b! ]0 u$ d T+ {6 U0 Y3 ` │ │ 05_The_Vector_Space-_Checksum_function.txt
) Y' A3 @% E) J │ │
( b! V* G. k( d0 F+ f │ ├─04_Week_3-_The_Matrix! M# w# @5 e5 U: S# W( O, }
│ │ 01_The_Matrix-_What_is_a_matrix.mp48 N! }3 V2 W2 i7 p( Z, t. t6 p
│ │ 01_The_Matrix-_What_is_a_matrix.pdf1 H4 t% d6 f. B; z
│ │ 01_The_Matrix-_What_is_a_matrix.srt' j$ G) Y8 |& F @4 b
│ │ 01_The_Matrix-_What_is_a_matrix.txt) ]! q* n, f$ @" f. f; ]
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.mp4
* J( S8 w" ~; B1 N2 ^! r │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.pdf
4 e0 x) T( F+ E @2 n4 v, }' r* T │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.srt! Z& y( N4 @( C7 Q/ _! h% e3 X
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.txt7 n: [* v ]9 s }: K2 _5 [
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.mp4, F: Q! a6 l; ]3 p" _- B
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.pdf
$ H5 ?* C4 k$ m: j2 J% r ^! ] │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.srt
+ r8 ^* H+ }4 b' A* f │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.txt
) E* ^1 c0 v( o" y │ │ 04_The_Matrix-_Null_space.mp49 z% ~8 {# u* ?
│ │ 04_The_Matrix-_Null_space.pdf, f2 |8 A4 @; |2 d3 Z, ?& P' P
│ │ 04_The_Matrix-_Null_space.srt0 }: H% [) B2 K) w; _
│ │ 04_The_Matrix-_Null_space.txt$ \' p# ]# _6 J3 s. x
│ │ 05_The_Matrix-_Error-correcting_codes.mp4
' k6 s/ l; x$ a │ │ 05_The_Matrix-_Error-correcting_codes.pdf6 c2 }" N5 b5 ]. ]& Y
│ │ 05_The_Matrix-_Error-correcting_codes.srt, Z, U, l8 M0 c; p
│ │ 05_The_Matrix-_Error-correcting_codes.txt
: r5 T2 o; e# _5 J │ │ 06_The_Matrix-_Matrices_and_their_functions.mp4" ?6 `0 V4 }+ c' j$ i5 z( o) G
│ │ 06_The_Matrix-_Matrices_and_their_functions.pdf8 B" Z* T3 g" N+ Q
│ │ 06_The_Matrix-_Matrices_and_their_functions.srt. G% A2 G7 i( V# b! @" G" p. ~
│ │ 06_The_Matrix-_Matrices_and_their_functions.txt
F- c ^! b o/ [ │ │ 07_The_Matrix-_Linear_functions.mp41 P' k/ L9 c$ I" `( @" E
│ │ 07_The_Matrix-_Linear_functions.pdf. S* w- L5 D0 K5 Z
│ │ 07_The_Matrix-_Linear_functions.srt+ I) k# G2 B4 w$ \* K% ]
│ │ 07_The_Matrix-_Linear_functions.txt' P% _* Y) ?; t( b$ v
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.mp4) i' b3 l) S' g$ v6 x
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.pdf
; A/ k6 L4 F) s( A3 w │ │ 08_The_Matrix-_Matrix-matrix_multiplication.srt& u$ H$ L1 `- o6 ~
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.txt
$ {; B" K, h6 z │ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.mp45 A+ j! I, v2 n! {8 i* y
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.pdf' c' {5 h( k8 L7 O1 I
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.srt' |4 u2 {0 u/ G4 x
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.txt. e$ Q- {' f; `' {, C7 U$ i
│ │ 10_The_Matrix-_Matrix_inverse.mp4 e7 _6 H m) x# i4 s: d, x
│ │ 10_The_Matrix-_Matrix_inverse.pdf: ]3 }* c7 t6 q. _( K
│ │ 10_The_Matrix-_Matrix_inverse.srt. u r) F+ u0 N8 |8 O- c* d1 ~
│ │ 10_The_Matrix-_Matrix_inverse.txt
2 _) |( ?5 y# p │ │
! D- p" b d# x$ s" u5 i" G' k6 S" P │ ├─05_Week_4-_The_Basis2 d+ k# g+ g0 J- V7 Z$ P
│ │ 01_The_Basis-_Coordinate_systems.mp4
4 o0 y# F+ G; { │ │ 01_The_Basis-_Coordinate_systems.pdf7 V- o8 j# O. ~( r5 ~8 n
│ │ 01_The_Basis-_Coordinate_systems.srt
& ]4 v$ ] m, _$ f2 ~ │ │ 01_The_Basis-_Coordinate_systems.txt
2 @$ c" a1 M: Y2 k/ G │ │ 02_The_Basis-_Lossy_compression.mp43 c' I) }/ f. [4 N- X! e+ k
│ │ 02_The_Basis-_Lossy_compression.pdf8 D/ y, Y% S9 U# b9 W5 X+ g: E
│ │ 02_The_Basis-_Lossy_compression.srt0 F6 N" r v) t% Q
│ │ 02_The_Basis-_Lossy_compression.txt
1 x7 t8 `9 S+ V: g& W │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.mp4
# P7 O1 Q2 {' ~1 B" j4 v │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.pdf: ~; ^% P4 g& B# e. N( _2 x
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.srt) I9 m' K9 X& _9 N$ v" M
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.txt" D9 X X$ n! i8 s
│ │ 04_The_Basis-_Minimum_spanning_forest.mp4
7 l+ d: x! _ Q- L7 L │ │ 04_The_Basis-_Minimum_spanning_forest.pdf' e. q! Y# q% ?* s6 b9 H
│ │ 04_The_Basis-_Minimum_spanning_forest.srt4 S/ k/ t' A2 @& [5 H+ s
│ │ 04_The_Basis-_Minimum_spanning_forest.txt, W- R2 d- h/ n( Z& J& D
│ │ 05_The_Basis-_Linear_dependence.mp4
! f9 ` B1 K2 g │ │ 05_The_Basis-_Linear_dependence.pdf8 G( u) r. I/ g5 T+ Z. d3 n
│ │ 05_The_Basis-_Linear_dependence.srt# ]1 o! `) f i w% v
│ │ 05_The_Basis-_Linear_dependence.txt+ Z2 e0 V$ S) a7 e9 t
│ │ 06_The_Basis-_Basis.mp4
. q4 H$ K, o! m& l; N │ │ 06_The_Basis-_Basis.pdf
. ^+ {+ o; C* H: g5 \ │ │ 06_The_Basis-_Basis.srt* {8 f. a6 D1 k8 R1 ~$ _
│ │ 06_The_Basis-_Basis.txt+ C* ]0 B0 [1 \$ Y7 X- l
│ │ 07_The_Basis-_Unique_representation.mp4
& S3 L8 M2 ]8 \2 C+ n3 J │ │ 07_The_Basis-_Unique_representation.pdf* \ F; x4 V8 F% M9 n4 K8 Y$ f
│ │ 07_The_Basis-_Unique_representation.srt" z- _/ e7 Y$ n S
│ │ 07_The_Basis-_Unique_representation.txt
; v( n1 W, G4 J- G$ M) _* W │ │ 08_The_Basis-_Change_of_basis.mp4, Z& e) v" \/ b0 E! A4 }
│ │ 08_The_Basis-_Change_of_basis.pdf( n5 w5 u$ D2 W
│ │ 08_The_Basis-_Change_of_basis.srt
7 ]8 P* E& W# ?5 M+ [ │ │ 08_The_Basis-_Change_of_basis.txt! u' q: n o) b
│ │ 09_The_Basis-_Perspective_rendering.mp4
8 f! x9 h5 ~ P) R; J │ │ 09_The_Basis-_Perspective_rendering.pdf& X% ~& r% w- ^( y$ F. Z" q* V2 F
│ │ 09_The_Basis-_Perspective_rendering.srt$ f# |& F9 t D6 v
│ │ 09_The_Basis-_Perspective_rendering.txt
' ^9 s" P, A* R: ~5 Q │ │ 10_The_Basis-_Perspective_rectification.mp40 ~8 E) x4 x* z- S: I% T
│ │ 10_The_Basis-_Perspective_rectification.pdf
2 |% {3 j( Y- P8 i- R: T0 o, h+ m5 A │ │ 10_The_Basis-_Perspective_rectification.srt
6 F, O* ~8 |" V( r) j( f │ │ 10_The_Basis-_Perspective_rectification.txt: B r; o7 N: x) D0 D
│ │ 11_The_Basis-_The_Exchange_Lemma.mp4
6 _8 |, h4 p& _- ?$ t( N │ │ 11_The_Basis-_The_Exchange_Lemma.pdf
( L/ b5 W" o ^4 ^- h0 G │ │ 11_The_Basis-_The_Exchange_Lemma.srt
! j. Z3 n& e" G# g9 z1 W │ │ 11_The_Basis-_The_Exchange_Lemma.txt
& i: Q: l: ? K1 q- v │ │
- J8 P" D! L$ }: H+ A1 z9 |' G( N │ ├─06_Week_5-_Dimension, g9 Q) M2 z% g" Z* B& K
│ │ 01_Dimension-_The_size_of_a_basis.mp4
! A8 a" ]6 }7 Z │ │ 01_Dimension-_The_size_of_a_basis.pdf
' v3 i# S M$ F6 j3 v │ │ 01_Dimension-_The_size_of_a_basis.srt; d- p% i% g# |- G, p
│ │ 01_Dimension-_The_size_of_a_basis.txt
0 v$ y- ^9 S, n │ │ 02_Dimension-_Dimension_and_rank_I.mp4: \; D, Z ]6 P& F. S5 I0 ]' {
│ │ 02_Dimension-_Dimension_and_rank_I.pdf. x( N! `- q4 ^
│ │ 02_Dimension-_Dimension_and_rank_I.srt6 e5 U4 O3 E! S, P
│ │ 02_Dimension-_Dimension_and_rank_I.txt1 D$ X7 i5 H% k/ |
│ │ 03_Dimension-_Dimension_and_rank_II.mp4
2 i) p# e: S2 O, c1 p# ` │ │ 03_Dimension-_Dimension_and_rank_II.pdf3 L" {6 S, P8 }4 a, C
│ │ 03_Dimension-_Dimension_and_rank_II.srt
! c$ k/ X9 e! g: _% i$ p │ │ 03_Dimension-_Dimension_and_rank_II.txt3 d- u* M# V/ }8 Q
│ │ 04_Dimension-_Direct_sum.mp4
) Z" D+ C" a2 p* A- N5 P │ │ 04_Dimension-_Direct_sum.pdf4 m4 b1 L0 t4 U/ D) w. k
│ │ 04_Dimension-_Direct_sum.srt
' d' p1 I+ z4 \5 I4 s │ │ 04_Dimension-_Direct_sum.txt: z: x; L1 T+ ?! Z: [7 r
│ │ 05_Dimension-_Dimension_and_linear_functions_I.mp4: T3 f, L p1 Z3 J4 g
│ │ 05_Dimension-_Dimension_and_linear_functions_I.pdf% C" |& a' M, ^) ^ g" o; x1 U9 R
│ │ 05_Dimension-_Dimension_and_linear_functions_I.srt/ b8 B2 K: G* m% v0 q& i
│ │ 05_Dimension-_Dimension_and_linear_functions_I.txt2 S9 T* E; }& T1 {' k
│ │ 06_Dimension-_Dimension_and_linear_functions_II.mp47 G( X* G9 ?# p. s& _* |9 N+ a b
│ │ 06_Dimension-_Dimension_and_linear_functions_II.pdf7 O0 v& L' o( m% l
│ │ 06_Dimension-_Dimension_and_linear_functions_II.srt
& T" {: j- Y$ B │ │ 06_Dimension-_Dimension_and_linear_functions_II.txt a2 E9 b' v3 f5 N3 j; F4 }
│ │ 07_Dimension-_Two_representations_of_vector_spaces.mp4) u3 b! V z5 u
│ │ 07_Dimension-_Two_representations_of_vector_spaces.pdf' T/ n+ S l1 U2 ], y8 \1 z
│ │ 07_Dimension-_Two_representations_of_vector_spaces.srt# f' {2 c5 \; d: y/ r$ z
│ │ 07_Dimension-_Two_representations_of_vector_spaces.txt1 M, }' ^* o' a# N: ?
│ │ 08_Dimension-_Threshold_secret_sharing.mp4
+ G( W2 D+ d+ H# S4 [8 k │ │ 08_Dimension-_Threshold_secret_sharing.pdf
/ Q9 Q9 a( H6 T# Q9 H h0 n0 G │ │ 08_Dimension-_Threshold_secret_sharing.srt
+ J! d( |1 B# Z │ │ 08_Dimension-_Threshold_secret_sharing.txt
$ t& b8 h- @/ F" f │ │ 9 t! Y- v' D# W5 g, L3 V
│ ├─07_Week_6-_Gaussian_Elimination_and_the_Inner_Product
; D! _$ |$ K$ M' K$ } │ │ 01_Gaussian_Elimination-_Echelon_form.mp44 w2 y+ t* B8 b# Y: I$ R
│ │ 01_Gaussian_Elimination-_Echelon_form.pdf! v% C5 C: A% A6 I9 F4 ~. D- M
│ │ 01_Gaussian_Elimination-_Echelon_form.srt, n6 y* R/ q: z: \6 V& ~
│ │ 01_Gaussian_Elimination-_Echelon_form.txt
2 T8 C' f% d4 R1 I* ` │ │ 04_Gaussian_Elimination-_Factoring_integers.mp4
3 L$ L6 e9 v) m* H │ │ 04_Gaussian_Elimination-_Factoring_integers.pdf
6 u( K/ J2 A3 X3 A$ u, x& l2 { │ │ 04_Gaussian_Elimination-_Factoring_integers.srt
$ V5 p4 t) V& h- K; u" N1 n │ │ 04_Gaussian_Elimination-_Factoring_integers.txt& ?2 X0 E2 U$ A' r5 G8 W
│ │ 05_The_Inner_Product-_The_inner_product.mp4+ y' w2 A% O* D6 @5 m
│ │ 05_The_Inner_Product-_The_inner_product.pdf& d+ t$ ?. {# ?6 c5 v4 P1 Y
│ │ 05_The_Inner_Product-_The_inner_product.srt
) F6 U& ]% E0 G5 N* q/ G0 r │ │ 05_The_Inner_Product-_The_inner_product.txt
. G! t3 Z3 t5 Q1 }* W4 Z% W0 y │ │ 06_The_Inner_Product-_Orthogonality.mp4
+ x; z7 g' U: [. V" `! S │ │ 06_The_Inner_Product-_Orthogonality.pdf' d) ?% c/ }8 H4 V
│ │ 06_The_Inner_Product-_Orthogonality.srt- E8 Q2 Z; m& k7 ^) e
│ │ 06_The_Inner_Product-_Orthogonality.txt
) O' C( K/ y: ~- T1 t │ │ # t0 d& e, ]1 J4 i$ }; f
│ ├─08_Week_7-_Orthogonalization
$ b! \6 O( B, T; g5 B3 L │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.mp4
, F" ?2 U) d+ p! }4 y │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.pdf5 @. T% @0 l; _8 @$ J3 J
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.srt6 D/ V+ g1 P/ Y0 H; H6 o* ]
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.txt
4 v6 K% }. {/ Q$ a │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.mp4! A7 O4 @( ~$ Q+ S
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.pdf d- {- b' X. U: p
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.srt2 o- ]( A) U, O9 m& v- L% y2 d( k
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.txt6 K$ Q& J }4 |7 @+ k
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.mp4/ T6 B+ e4 f" _' h7 z, p$ F
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.pdf
: u3 A1 O o! O. N │ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.srt2 S2 M% t1 b4 [/ e5 }
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.txt% [5 N, s* E: v: I
│ │ 04_Orthogonalization-_Computing_a_basis.mp4( _+ m- K% K" i# f/ E, n. k
│ │ 04_Orthogonalization-_Computing_a_basis.pdf
2 Q' l) z# F- \9 D) |7 k │ │ 04_Orthogonalization-_Computing_a_basis.srt; A; d8 w1 A% a
│ │ 04_Orthogonalization-_Computing_a_basis.txt8 t- j8 S: Y* Z6 L1 o- r* p+ C: A
│ │ 05_Orthogonalization-_Orthogonal_complement.mp4; }' ^! a7 }. o% E
│ │ 05_Orthogonalization-_Orthogonal_complement.pdf
8 @, v P: p+ b, D/ S8 j1 R │ │ 05_Orthogonalization-_Orthogonal_complement.srt8 ~/ ?( q1 _! s2 I# K6 ?7 o) J0 f: x
│ │ 05_Orthogonalization-_Orthogonal_complement.txt
: Q P8 w+ [. p/ D+ x7 d" s │ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.mp4; d) ?5 {# C2 [/ m: d6 |
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.pdf6 [2 b4 Y0 a7 N; T9 a+ f+ B. g# Q
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.srt# G# `( J: ?# Y/ M
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.txt
" T& L/ l1 w: r │ │ 07_Orthogonalization-_The_QR_factorization.mp4; u8 W0 j3 Y& E9 j; T2 K# X
│ │ 07_Orthogonalization-_The_QR_factorization.pdf
+ ~, ]% N3 D. ^/ x3 m │ │ 07_Orthogonalization-_The_QR_factorization.srt
' z2 q: j }$ l9 [8 I8 S" ?( B T8 T │ │ 07_Orthogonalization-_The_QR_factorization.txt# j: @; c2 j6 ^1 n- r) Z2 u: B6 e
│ │ 09_Orthogonalization-_Applications_of_least_squares.mp4
4 J; W! U4 S8 h; @' | │ │ 09_Orthogonalization-_Applications_of_least_squares.pdf
. v+ r7 h) @% V9 x# E5 \ │ │ 09_Orthogonalization-_Applications_of_least_squares.srt
1 k" `' c6 j# F3 v, E9 Y │ │ 09_Orthogonalization-_Applications_of_least_squares.txt
' r4 I0 g5 v. S6 J │ │ 2 L0 o) T( p# c
│ └─09_Tutorials" `; f. X( S5 s5 {
│ 01_How_to_submit_assignments.mp4
& z. Q) n9 o# Q% o0 C7 w) X, _ │ 01_How_to_submit_assignments.srt
- e; f# T W& U: O0 m% i: h │ 01_How_to_submit_assignments.txt
9 Q5 F4 g, O/ O& Q │
' A5 g8 f9 J+ z5 J, i3 X0 k( z5 L0 @ ├─Big Data, Cloud Computing, & CDN Emerging Technologies
* `- C1 I5 H6 }. l/ j Q( F6 S │ └─Big Data, Cloud Computing, & CDN Emerging Technologies
, n( l# A5 x8 k7 v │ │ 下载说明.txt2 E$ x( h0 w. P1 I5 o
│ │ 关注我们.png3 Z }9 D2 O: ^3 K( C( _
│ │ 攻城狮论坛=网络技术+编程视频.url& r0 F$ L+ }9 H5 A3 Y' C3 U9 g& i$ e
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
/ s8 C: k. Q n$ _$ U% D- _ │ │ % |* |7 T H9 j5 A, T" t' u
│ ├─01_cloud-computing' L$ ?. v) s+ |9 w
│ │ └─01_cloud-computing/ T; v/ t' X$ V$ ]+ n- Y
│ ├─02_big-data
: ~* ^: m& w% R. a. Y6 T, v$ b: E │ │ └─01_big-data9 y+ U8 n7 p! _, R. ^! B% w
│ │ 01_big-data-examples.mp4
0 i" z, O/ T: G$ c │ │ 01_big-data-examples.srt3 r( h* C7 s" I/ _5 `! N; t
│ │ 02_big-datas-4-vs.mp40 t; G' k! E* k- B8 k% H& @" r
│ │ 02_big-datas-4-vs.srt
- t) @' P- S% x) [ │ │ 03_hadoop.mp4
0 y' P9 O; s) C- j3 _ │ │ 03_hadoop.srt3 F1 F9 k4 V, o# k4 w
│ │ 04_mapreduce-vs-rdbms.mp4; X* j- ?; {, L9 ?8 m2 u
│ │ 04_mapreduce-vs-rdbms.srt
9 k. \7 K m% i3 t │ │ 05_mapreduce.mp4 q* N2 D2 E3 j- C
│ │ 05_mapreduce.srt! O9 N3 c/ V) k4 T! y, ]4 ]2 r+ Y
│ │ 06_hdfs.mp4
/ G! U2 X' s- u. e% u z │ │ 06_hdfs.srt
& r9 o) P/ j$ ?! t, O │ │ x7 f3 Q( Q! u m& }( e i
│ └─03_cdn-content-delivery-network
" C* s5 t9 x3 q+ a0 n- N3 ? ├─Duke Image and video processing From Mars to Hollywood with a stop at the hospital- O5 c" V4 } l
├─Interactive Computer Graphics
$ m9 J r$ I) B2 E$ w# u │ └─Interactive Computer Graphics3 T: Y' z4 b2 n* e
│ interactivegraphics-Torrent-Week1_All.zip.torrent& G1 ]- I) J% A+ u
│ interactivegraphics-Torrent-Week2_All.zip.torrent
% S8 m+ w. }( d1 B │ interactivegraphics-Torrent-Week3_All.zip.torrent
3 D, D# g5 ~ H' o │ interactivegraphics-Torrent-Week4_All.zip.torrent4 N& @6 U# @8 q8 f
│ interactivegraphics-Torrent-Week5_All.zip.torrent% x4 n( g* L X$ y
│ interactivegraphics-Torrent-Week6_All.zip.torrent
. t3 j O; Y. J$ \ }1 y │ interactivegraphics_Torrent_Week1_All.zip, G; y. U) n; v( m) V/ ~" @- ^
│ interactivegraphics_Torrent_Week2_All.zip
q8 R: `1 P0 {/ _' x- F$ ]' s+ J- i │ interactivegraphics_Torrent_Week3_All.zip
0 b" n. Y7 c& e. P │ interactivegraphics_Torrent_Week4_All.zip
L1 U; f+ N. ^9 J0 `! U6 a+ J- ? │ interactivegraphics_Torrent_Week5_All.zip. [. L+ d" P, Y$ |- q5 j, A! R: {
│ 下载说明.txt
7 I( w* w: d+ k2 j │ 关注我们.png
! R- y8 i p$ H2 j2 K+ Z5 m │ 攻城狮论坛=网络技术+编程视频.url
7 h0 c& y: b% T4 u4 S' _% v │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
+ | t) D3 d- w │ ; _, l; S4 ]/ H9 G3 r9 @& |
├─Malik - Computer Vision- L: J- t" Z. @5 ]+ I! D
│ └─Malik - Computer Vision1 K* {8 a1 {; X/ D# U5 m3 V
│ 001_Overview.mp4
) X: y/ H) [7 V0 m8 o │ 001_Overview.srt9 v5 N2 B' `* x
│ 002_Fundamentals of image formation.mp4, e' g' B: @2 G8 y* V0 z6 A3 u9 `
│ 002_Fundamentals of image formation.srt
3 w8 M/ A2 r, P1 O/ N │ 003_Fundamentals of image formation - part II.mp4
( @; n0 Z: H, e. S# d" f │ 003_Fundamentals of image formation - part II.srt
; B4 p* c3 L# N/ N │ 004_Rigid body motion.mp4
2 Q2 ~4 _$ w7 J# w' E, W' S │ 004_Rigid body motion.srt: [2 ^- W) I v: ~
│ 005_Orthogonal transformations.mp4% D$ _) D5 S/ c3 U/ I; l! Z9 k8 D
│ 005_Orthogonal transformations.srt7 E r/ x; Q' g6 I7 X# e' Z
│ 006_Orthogonal transformations - Orthogonal Matrices.mp47 l d2 q. q' s9 k- \9 \
│ 006_Orthogonal transformations - Orthogonal Matrices.srt
( G. A# y& j/ Z+ K( S Y. X! q │ 007_Orthogonal matrices - Rotations and reflections.mp4
% V2 `' `* T& J/ [ │ 007_Orthogonal matrices - Rotations and reflections.srt3 k9 k0 B# V/ Y* e! c4 e
│ 008_Parametrizing Rotations in 3D.mp43 S( ^! |2 i. T
│ 008_Parametrizing Rotations in 3D.srt( ]( n+ i8 c5 z- I1 n! g; }
│ 009_Euclidean, Affine and Projective Transformations.mp4
! g0 z% K, y1 I z, w │ 009_Euclidean, Affine and Projective Transformations.srt
" r+ J( d2 i/ [, ~1 a( M │ 010_Dynamic Perspective - I [15 mins].mp4' P/ U! w' d2 n H; t0 ^" r1 M$ L
│ 010_Dynamic Perspective - I [15 mins].srt
! F; o8 f- F: s* m# }/ B" J │ 011_Dynamic Perspective - II [25 mins].mp4
0 Q) ^5 }4 G' @, K# R1 U │ 011_Dynamic Perspective - II [25 mins].srt% h+ ]2 B0 d9 j$ }! s
│ 012_Binocular Stereo I [23 mins].mp44 T) Z) k6 W/ R' s4 |# Q' J$ U& \* y' E
│ 012_Binocular Stereo I [23 mins].srt
9 S9 W# S3 F8 |0 }4 R7 @# R │ 013_Binocular Stereo II [17 mins].mp4
3 W8 i6 Q5 l6 r& j0 k- A3 y8 X │ 013_Binocular Stereo II [17 mins].srt
' o1 r! q( }% S2 R( L; K │ 014_Binocular Stereo III [13 mins].mp4! z# b: Z0 N' ]
│ 014_Binocular Stereo III [13 mins].srt7 y. I1 Q" E3 {( G% @
│ 015_Binocular Stereo IV - The Essential Matrix [26 min].mp4, \4 z$ G" c# p5 a4 T# X
│ 015_Binocular Stereo IV - The Essential Matrix [26 min].srt
8 s! \( ]' H0 G6 U2 `$ Y │ 016_Radiometry.mp4
" M5 T6 _" i( V% w │ 016_Radiometry.srt# N1 m) J" H4 ^
│ 017_Image processing.mp4
$ j. S x9 c7 V; N' l ] │ 017_Image processing.srt7 J. b0 ]6 g! N8 h( v5 T& B
│ 018_Image Processing2.mp4
; X0 n$ P( h$ }* w3 }, x │ 018_Image Processing2.srt
9 E6 I8 }- y9 w" M/ E% ?" b │ 019_Orientation histograms [7 min].mp40 h! B. a# [: ^
│ 019_Orientation histograms [7 min].srt- c2 l0 A/ M4 f9 Z1 S# L
│ 020_Handwritten digit recognition - Introduction.mp45 B. L4 q! u* H5 Y, x
│ 020_Handwritten digit recognition - Introduction.srt. h7 ` `2 `6 h8 B) A
│ 021_Support Vector Machines.mp47 r- |8 ]6 k/ v- r; P
│ 021_Support Vector Machines.srt9 l# |# Y7 U- P6 q% P$ p1 W
│ 022_Transformation Invariance and Histograms.mp4
8 R; g& @! z& D& J3 N │ 022_Transformation Invariance and Histograms.srt
9 r6 u/ b4 _ ], x │ 023_Digit recognition using SVMs.mp4% V7 u% U6 |/ k. y1 e$ y0 E
│ 023_Digit recognition using SVMs.srt, {6 r, V4 p/ V/ s5 P; M1 T; X Q
│ 024_Random forests.mp4+ F* @- g/ p$ j! p H, u+ o* @
│ 024_Random forests.srt3 x1 ]% y1 u! d7 Y( O4 n$ F( c! X
│ 025_Detection of 3D objects.mp4
" U( A8 J; w0 l/ }- U$ \ │ 025_Detection of 3D objects.srt1 q3 P2 z) U8 L q- z* c$ R
│ 026_Concluding Remarks.mp4$ o- G! c7 Z6 n- ~, R! q. }, Q
│ 026_Concluding Remarks.srt4 E* _3 P% s4 ^
│ 下载说明.txt
( g. U8 Y9 v m4 q, r │ 关注我们.png
: D% K/ C& l# Y, v: f* @ o │ 攻城狮论坛=网络技术+编程视频.url
* f3 F6 y- {' D9 g( n │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
- t: }& Y7 G S) S! z% E │
: N' E! t1 D- n$ ~( s7 D! I+ }7 j& F; g ├─Practical Machine Learning
1 |8 M$ _# o0 ?" \4 v" V. p │ └─Practical Machine Learning
8 U5 X, b4 f Z d r; N( W │ │ 下载说明.txt
. u4 y+ k0 E/ z1 U; M │ │ 关注我们.png( g1 S# Q- T @4 i8 L
│ │ 攻城狮论坛=网络技术+编程视频.url
* F" O+ d) z+ b9 V │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt* ^- ?# P- V. p3 b. _# Q
│ │
. H4 j$ P: ^% Q' A) e+ [/ X2 n. N │ ├─Week1. |' T& m2 `# @3 |$ N9 ]! l+ p8 f
│ │ 001predictionMotivation.pdf
+ p+ u" v' U* |& B# g# R* ?) o: t │ │ 002whatIsPrediction.pdf" Z+ n8 A$ l D6 @ y: A! d- ^
│ │ 003relativeImportance.pdf2 c3 v/ c* F! q$ L
│ │ 004inOutSampleErrors.pdf
1 I) b6 X) f9 `- q2 V! N; c0 P │ │ 005predictionStudyDesign.pdf
0 y. y# A* [! U8 l7 Q/ S3 v& y* G │ │ 006typesOfErrors.pdf* D3 g: M$ `- _8 F! R8 k
│ │ 007receiverOperatingCharacteristic.pdf# h) L K+ r* V. P" @
│ │ 008crossValidation.pdf4 a$ A$ K" M, f+ F& X
│ │ 009whatData.pdf
0 C; {( B' a) Q9 ^/ } │ │ 1 - 1 - Prediction motivation (8-26).mp48 a- q; l% Q4 K0 @2 o9 P2 ~
│ │ 1 - 1 - Prediction motivation (8-26).srt
5 y9 c7 z1 \9 v! J7 G6 ^0 T │ │ 1 - 2 - What is prediction- (8-39).mp4. G6 L# l5 v) a3 F/ h. d
│ │ 1 - 2 - What is prediction- (8-39).srt" M, L1 l2 L, ^& t/ y7 z
│ │ 1 - 3 - Relative importance of steps (9-45).mp4
1 X2 s. E9 S3 d( d& h │ │ 1 - 3 - Relative importance of steps (9-45).srt
# N w- y% X9 [* Z/ q& P& q │ │ 1 - 4 - In and out of sample errors (6-57).mp4( p- ]7 b3 x8 W. x! C/ M I$ w
│ │ 1 - 4 - In and out of sample errors (6-57).srt
" J9 ^; C$ o5 L' P │ │ 1 - 5 - Prediction study design (9-05).mp4/ J( y$ h1 r& V) I8 ?* m$ V5 {
│ │ 1 - 5 - Prediction study design (9-05).srt; E; h* {! J R8 Y
│ │ 1 - 6 - Getting Data Overview (1-34).mp4
: k8 m8 ? H' G$ } ~5 P) L. P/ C │ │ 1 - 6 - Types of errors (10-35).mp4
6 [' z: s( M; q" C% T5 z/ }. c# O │ │ 1 - 6 - Types of errors (10-35).srt# B% x$ ^9 L( u4 P: t, F
│ │ 1 - 7 - Receiver Operating Characteristic (5-03).mp4
. w' B- @6 ~" K8 d, v │ │ 1 - 7 - Receiver Operating Characteristic (5-03).srt
/ p; }9 D Y: v, G │ │ 1 - 8 - Cross validation (8-20).mp4
# C' C" H! O4 J" A7 l │ │ 1 - 8 - Cross validation (8-20).srt
0 @* j( H3 l$ Y5 {9 g) b: ] │ │ 1 - 9 - What data should you use- (6-01).mp48 l8 u3 L) R9 N% _; [; p% x" Q
│ │ 1 - 9 - What data should you use- (6-01).srt/ M0 \, A V2 c' A, V
│ │ 0 B6 }' Z0 l9 i. V% ?
│ ├─Week27 B6 ?, `8 ?3 {! l: i
│ │ 010caretPackage.pdf
, r1 q/ |( E8 U │ │ 011dataSlicing.pdf
& t: B, x6 P6 T" m0 P6 K │ │ 012trainOptions.pdf; a+ t X0 k1 J
│ │ 013plottingPredictors.pdf
: t k+ _! @ L6 J# T& H │ │ 014basicPreprocessing.pdf
% Y! `% R5 m5 _# ^' w$ q- D │ │ 015covariateCreation.pdf
8 ^! `% r+ I3 X) l │ │ 016preProcessingPCA.pdf) ^0 [) M; G+ n$ X
│ │ 017predictingWithRegression.pdf- ^1 d; R) `% r7 p7 F. y" |
│ │ 018predictingWithRegressionMC.pdf
& N, e3 N( W; g' ]; i3 o, G$ Y │ │ 2 - 1 - Caret package (6-16).mp4
; v* K" ]/ \0 @ │ │ 2 - 1 - Caret package (6-16).srt
/ G# J6 @$ Z0 D" I: v │ │ 2 - 1 - Caret package (6-16).txt0 U* \9 g; K/ K7 B. g8 ^, h
│ │ 2 - 2 - Data slicing (5-40).mp4- }0 {8 R K+ D2 X- Y
│ │ 2 - 2 - Data slicing (5-40).srt, P+ T1 M: K7 {. z d
│ │ 2 - 2 - Data slicing (5-40).txt' n) ?3 t( @4 p: o% _; d0 A9 D
│ │ 2 - 3 - Training options (7-15).mp4
. D5 c, _% z) D3 K │ │ 2 - 3 - Training options (7-15).srt
1 z# B) a* c, u7 h* B │ │ 2 - 3 - Training options (7-15).txt/ ^+ g. B- b& G/ _! O
│ │ 2 - 4 - Plotting predictors (10-39).mp4
; T% \8 t5 x# U, }, V4 O$ `5 z │ │ 2 - 4 - Plotting predictors (10-39).srt3 O! @3 O) S6 T% I$ a- ~* B U4 _
│ │ 2 - 4 - Plotting predictors (10-39).txt: |" R0 Z' Z' g) N5 t9 m- M; v: J
│ │ 2 - 5 - Basic preprocessing (10-52).mp43 t, s- b. b; E7 N' W
│ │ 2 - 5 - Basic preprocessing (10-52).srt
' Y4 Z9 t# d* Y) L. f │ │ 2 - 5 - Basic preprocessing (10-52).txt0 J3 [0 Y; h9 t- |
│ │ 2 - 6 - Covariate creation (17-31).mp4# v) a0 A' ^( B7 q) I1 @; f
│ │ 2 - 6 - Covariate creation (17-31).srt
0 i: D5 B6 @. |1 U/ N: S │ │ 2 - 6 - Covariate creation (17-31).txt$ ~' o6 p0 |+ T3 N/ \7 j4 P
│ │ 2 - 7 - Preprocessing with principal components analysis (14-07).mp4
5 Z5 t" b! E8 I │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).srt8 ~/ m6 }1 }# y9 b0 U
│ │ 2 - 7 - Preprocessing with principal components analysis (14-07).txt
; c2 h% _/ G9 e. g, q1 j& Z$ J- B. @+ ? │ │ 2 - 8 - Predicting with Regression (12-22).mp4
) l- p3 y% f2 v$ V8 f0 C u │ │ 2 - 8 - Predicting with Regression (12-22).srt0 e- x/ c9 j& f0 `
│ │ 2 - 8 - Predicting with Regression (12-22).txt5 P* ~ a8 h& H+ g
│ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).mp4
) a- O! M' x* D* K; d │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).srt( D2 z" g8 G5 ]9 T" D0 F9 D: @. B \
│ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).txt
4 u6 i- y5 X5 ?& K% y │ │ * K1 }, T# s% e- t7 \7 }3 }
│ ├─Week3& `: e7 D& P3 t( K) h2 `* {* N
│ │ 019predictingWithTrees.pdf5 z( J5 o7 u: O* s
│ │ 020bagging.pdf
5 v4 s5 }- [* b/ i8 f │ │ 021randomForests.pdf3 ]# V2 T4 ], w# |! y1 r
│ │ 022boosting.pdf+ {( Y1 v/ Q5 b. B# j/ ]
│ │ 023modelBasedPrediction.pdf. p" H7 O3 ]% D0 f& x
│ │ 3 - 1 - Predicting with trees (12-51).mp4
, H% [/ t( I, _: J │ │ 3 - 1 - Predicting with trees (12-51).srt
$ h" j c2 L$ A t │ │ 3 - 1 - Predicting with trees (12-51).txt
- s7 g' h/ i) U2 b" y5 o │ │ 3 - 2 - Bagging (9-13).mp4) O1 l/ l4 L$ S6 \
│ │ 3 - 2 - Bagging (9-13).srt2 p# Y! u0 ]: E% X% S) W: `8 |
│ │ 3 - 2 - Bagging (9-13).txt
- X' j! c5 K; f8 ? │ │ 3 - 3 - Random Forests (6-49).mp4
3 S; q8 l/ I6 e6 s4 G │ │ 3 - 3 - Random Forests (6-49).srt% e( \1 @4 l/ ?% c3 B' w+ o0 F; z5 J
│ │ 3 - 3 - Random Forests (6-49).txt
( N: U+ S5 V G. {- E" u/ { │ │ 3 - 4 - Boosting (7-08).mp4( S1 E7 O1 t# Z' l3 E0 }1 ^2 n6 b" T
│ │ 3 - 4 - Boosting (7-08).srt
! O" ]7 }5 T- e$ |2 Q% S │ │ 3 - 4 - Boosting (7-08).txt0 N4 T4 b& J8 _
│ │ 3 - 5 - Model Based Prediction (11-39).mp42 L9 {9 m% i$ v+ F' M8 v0 C
│ │ 3 - 5 - Model Based Prediction (11-39).srt
" a& M! p# o5 D* [2 c" H │ │ 3 - 5 - Model Based Prediction (11-39).txt
0 q) u+ o* e* q' T) j │ │
$ w! Y0 }* f: Y; q3 A │ └─Week4
# D, `+ }5 }- `0 s$ ?9 P │ 024regularizedRegression.pdf
* f! A! f' b, q3 [, v" t │ 025combiningPredictors.pdf& ]2 Z3 Z1 y6 j6 N; J! w
│ 026unsupervisedPrediction.pdf
0 o+ t9 k/ O1 ?. a% V% y │ 027forecasting.pdf% _8 D' F1 r1 L% b+ G* P: }! S: c
│ 4 - 1 - Regularized regression (13-20).mp4
! _8 M& R. Z* e │ 4 - 1 - Regularized regression (13-20).srt
& X: Z3 J' x9 h5 g │ 4 - 1 - Regularized regression (13-20).txt
4 b3 @* H, f h, V" I; Z4 m }3 } │ 4 - 2 - Combining predictors (7-11).mp4
% q% Y; V( g+ E& J" l │ 4 - 2 - Combining predictors (7-11).srt5 M* P$ m) V) |0 C7 V
│ 4 - 2 - Combining predictors (7-11).txt
" w/ s8 z* K' V* ]5 e │ 4 - 3 - Forecasting.mp4/ f5 m2 R6 s* b/ m1 y% F& c
│ 4 - 3 - Forecasting.srt
v( G5 u6 z5 p7 _ │ 4 - 3 - Forecasting.txt p$ [1 m X7 @! E2 F3 A
│ 4 - 4 - Unsupervised Prediction (4-24).mp4
* r( f+ f/ |# W# \ E │ 4 - 4 - Unsupervised Prediction (4-24).srt
1 A8 m3 I' W8 A5 r H │ 4 - 4 - Unsupervised Prediction (4-24).txt
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├─Sapiro - Image and video processing( r" v9 k5 S6 `6 Y1 e6 X# N7 h
│ 01.pdf
4 T4 [! a6 H, ]; Y7 D │ 02.pdf
& F" E; G" A% }+ c( [0 A4 q | u │ 03.pdf4 j9 n# B+ \9 }
│ 04.pdf
+ Y/ P2 U$ Q1 U% I% b" P. }. h( N │ 05.pdf# L2 ?: I G4 ?5 M1 D
│ 06.pdf
6 m; i% b! j, l# I │ 07.pdf
/ {5 s$ q# l' F1 Y4 U# _ │ 08.pdf
& H9 A3 C' x! @3 Z │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.mp42 y% m5 @2 Q7 v! W2 f" [; }
│ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.srt
6 m; F& n8 S* ~1 T) s │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.txt
0 Q, h+ S) E, \7 }) B │ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.mp4- D, P# R8 d* w7 B, {
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.srt# _( A! d! t' y& T* n# Q4 H
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.txt; c* |, F( x; }% b
│ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.mp4
! m1 u0 X; o2 Q( s! O │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.srt
+ \6 X; I. Y; ^- t( H │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.txt
7 B" j4 Z2 L5 ^" N$ w1 [ │ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.mp4# T9 j: n4 O+ i; a! [; }
│ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.srt
6 J5 p6 ]7 |. }% X1 \ │ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.txt
7 j5 H, X" H( H) ~ │ 10 - 5 - 4 - Thanks.mp4
' g: B+ o+ x7 b8 L; K │ 10 - 5 - 4 - Thanks.srt: Y+ B7 k3 i6 |4 J8 n; f3 \
│ 10 - 5 - 4 - Thanks.txt9 y7 y y+ ?& j. ]6 s3 L% A
│ 2 - 1 - 0 - Welcome and Start Here.mp4& \7 |* V. u* ^1 }1 {& \7 E
│ 2 - 1 - 0 - Welcome and Start Here.srt
# k9 [- k3 [6 x+ `# ^ │ 2 - 1 - 0 - Welcome and Start Here.txt
6 e" z2 V6 F/ |- Z, I1 S │ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.mp4* Y0 P. z h" q. `2 Q2 ~% o* i0 R
│ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.srt
7 t- w: L( U7 p, g, T$ m │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.mp4
# n# f1 Z" r4 i5 p d [ │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.srt
) X) x# O: b! g3 w% q( v! H │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.txt
# \. z* R9 q2 S$ n │ 2 - 4 - 2 - Course logistics - Duration 0242.mp4& ~. L0 u: P4 ~; P
│ 2 - 4 - 2 - Course logistics - Duration 0242.srt
- Q( F/ s" t$ g$ L: m │ 2 - 4 - 2 - Course logistics - Duration 0242.txt
! d4 ^" o" M/ P0 w │ 2 - 5 - 3 - Images are everywhere - Duration 0631.mp46 R) N/ B( z6 R6 C/ m, Y
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.srt
; _. ?' o8 ^$ h! k │ 2 - 5 - 3 - Images are everywhere - Duration 0631.txt5 W7 L. [9 Y/ O9 t0 u
│ 2 - 6 - 4 - Human visual system - Duration 1710.mp41 o- }7 ?/ [& H% N* n6 g
│ 2 - 6 - 4 - Human visual system - Duration 1710.srt
; r4 m* p: o( n% U3 u │ 2 - 6 - 4 - Human visual system - Duration 1710.txt$ T4 v* F% R& _7 }9 G9 h2 [
│ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.mp43 Q* @8 K, m) k% _* p+ _8 H
│ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.srt, ]/ f. R- [: F9 T* T" z
│ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.txt' z) @; G% M2 r, P2 e7 ^
│ 2 - 8 - 6 - Simple image operations - Duration 1705.mp4
( H, X+ f# d# @: q2 h │ 2 - 8 - 6 - Simple image operations - Duration 1705.srt
2 D! o, r7 ~. \0 v │ 2 - 8 - 6 - Simple image operations - Duration 1705.txt
! g. q9 ]3 |4 d$ C/ u9 E! W │ 3 - 1 - 1 - The why and how of compression - Duration 1416.mp4- ]. @ G4 T( m/ }+ V) G- o% |
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.srt {, X9 I) E2 k' @. I: ?, Y
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.txt, Y3 ^2 `5 R; v5 j
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.mp4
4 T% h c7 t2 w. ^ │ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.srt' F, d% e; A; _
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.txt
: U6 c, F9 b) ` │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.mp4# O4 ~8 a/ G6 o4 s+ D
│ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.srt' J' L4 J$ t% g" R: F/ F
│ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.txt$ T* G" o: e5 T3 y" Q. W
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.mp4
/ i4 [1 z6 p1 w& S$ B5 J │ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.srt
0 i; ?6 O D( i* a7 A9 U │ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.txt
" v+ C+ c e+ V- B$ ^ │ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.mp4
& L9 F# ]7 B3 H8 l/ t% b V: m │ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.srt3 _6 }4 N' t- ^) `, |2 R
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.txt
v) v* [. Q- L( F+ { │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.mp4# K0 J' }; g* r9 P+ W! D
│ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.srt1 g% b4 d* T' d6 f' p
│ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.txt; g- S. J: }/ I- t" ~
│ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.mp4
/ H3 i( l* i6 H1 [. Y7 Q2 F1 O │ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.srt
* K! |8 Q" F) l% _! {( R │ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.txt7 T2 s- R% w% ~/ W8 I3 V
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.mp41 E$ p* S9 v" P5 \% L9 U1 T
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.srt3 A2 W' j2 M' d% I5 r, C" c+ N
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.txt, ^4 x1 N7 v- X9 N j% z) a; L
│ 4 - 10 - 10 - Demo - Median filter - Duration 0131.mp4
4 m8 K1 B4 {" Z3 O. b m │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.srt
- \0 r/ h' _$ z9 u: X │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.txt
0 X, c9 D# ?% i' G$ T. K" y) i │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.mp4
1 r. \, c+ h1 G7 L │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.srt
, n' t' F9 z4 H+ n/ P( O+ p+ t6 m │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.txt
- f. L }7 E: X1 u │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.mp4+ T+ Q; g" l& R2 H5 ]! v a
│ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.srt% M' \7 p; B( O0 c; @0 a, b/ {
│ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.txt% W3 \, r& E) o; W% b" v! Z
│ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.mp4
. }$ w. F& a) H6 w% Z │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.srt
& `6 i& \# N. b; l │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.txt) P4 Q+ D$ f1 O* U; }1 W: E( n5 q
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.mp4" R6 w( ]* s$ a
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.srt
7 l+ R/ [$ Y2 B7 b; ~ │ 4 - 14 - 14 - Concluding remarks - Duration 0112.txt
2 `; N2 O6 n% c0 V( F3 k │ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.mp4. o+ d9 r# W5 ~% A3 u% i* \2 `
│ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.srt
7 w. y9 j9 I& i- j) g3 e │ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.txt
- v% T( ~* X8 A9 h, p │ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.mp4
, {3 ^4 B# d; ]- b5 k# a0 h │ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.srt& n8 B8 O0 q( r8 H( W: S
│ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.txt% g6 m# \1 R4 d. T& Q+ ~# P
│ 4 - 4 - 4 - Histogram matching - Duration 0831.mp4
6 n8 E- b4 o& B │ 4 - 4 - 4 - Histogram matching - Duration 0831.srt
. f: Z- A7 r. i' [* I │ 4 - 4 - 4 - Histogram matching - Duration 0831.txt- t5 W% w* @+ v, W
│ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.mp4
& D" `* m# J2 |; F% v, m │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.srt5 a. X* e3 r6 s' n4 \4 E" i) ?
│ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.txt' x B3 i" b3 T. a; y8 x
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.mp40 g/ s0 M$ s3 b7 H5 h6 J! l
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.srt3 V" j& U- i8 J3 a; B$ a
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.txt
/ S0 m4 u s4 \# b9 i │ 4 - 7 - 7 - Non-Local means - Duration 0727.mp49 U0 f4 _, h) @
│ 4 - 7 - 7 - Non-Local means - Duration 0727.srt
, n! ` c5 E. X6 S* A; O' `/ E. M7 s │ 4 - 7 - 7 - Non-Local means - Duration 0727.txt% i2 k \; c5 _6 j6 H; T
│ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.mp4! |; B/ u0 y4 o" [% g: y
│ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.srt
2 c1 k2 }& u, s7 f8 K7 K% x, J │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.txt E# h( e' C, h- W8 y3 m0 L' @4 E
│ 4 - 9 - 9 - Median filter - Duration 0720.mp4
. s5 e4 |5 b* g& U) w; \1 V2 r2 J: U5 f │ 4 - 9 - 9 - Median filter - Duration 0720.srt
9 H% \5 a2 P# i, g! Z6 u% ~ │ 4 - 9 - 9 - Median filter - Duration 0720.txt
_( y; K/ U$ z6 h: e5 v% V( B$ n │ 5 - 1 - 1 - What is image restoration - Duration 0749.mp4
( F% x$ u/ n4 O1 v6 ~. ~* U3 ? │ 5 - 1 - 1 - What is image restoration - Duration 0749.srt/ Y. P7 y; u, T
│ 5 - 1 - 1 - What is image restoration - Duration 0749.txt. ]4 c( O( x) n ?3 d
│ 5 - 2 - 2 - Noise types - Duration 1243.mp4
6 M. v& e( L8 U( [: Q6 f1 C │ 5 - 2 - 2 - Noise types - Duration 1243.srt3 b' ~2 T4 ?2 U1 U' V
│ 5 - 2 - 2 - Noise types - Duration 1243.txt
; v' W6 [, _5 V& ]7 O │ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.mp4
* M+ N! h* { b) b0 U/ g7 j │ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.srt
& s3 I' e% Z# D1 h: O │ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.txt
; ^& `, i$ C6 u/ l( _ │ 5 - 4 - 4 - Noise and histograms - Duration 0452.mp4
% k( |, G+ ^5 M: c( u0 n" q │ 5 - 4 - 4 - Noise and histograms - Duration 0452.srt( z! y8 L3 B: o0 N% N7 E
│ 5 - 4 - 4 - Noise and histograms - Duration 0452.txt! M1 P/ J; N$ \ Q P) z9 u
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.mp4
j7 w/ M* y: m& _% n │ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.srt
/ U1 |0 S. A/ P' S: G │ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.txt
* A# S1 L7 F$ v │ 5 - 6 - 6 - Degradation Function - Duration 1140.mp4
+ g( r& y* W$ ^' \1 I3 _, z" B │ 5 - 6 - 6 - Degradation Function - Duration 1140.srt; w' [: Y* q% f- ? a% J
│ 5 - 6 - 6 - Degradation Function - Duration 1140.txt
0 _( X# N+ U* l+ n; ` │ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.mp4# ~, g y2 j$ `' l0 {& U- I
│ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.srt
& O _+ z1 v" ~. m7 h │ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.txt$ Z9 {& o- @9 z1 F, i' p2 j
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.mp4
. ]% k7 R% Y* z. E │ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.srt6 J" i% b1 x4 U- B( k" t K" b
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.txt
: h, G l( u) C3 P% V$ p │ 5 - 9 - 9 - Concluding remarks - Duration 0033.mp4
p2 K& y5 [0 k- ^$ m4 `+ e/ j │ 5 - 9 - 9 - Concluding remarks - Duration 0033.srt
, U2 ]9 [3 i5 H4 u: e* ] │ 5 - 9 - 9 - Concluding remarks - Duration 0033.txt
3 I% h5 T5 x5 P% i: ~3 G; {6 M' p │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.mp40 n3 |% u, j; e3 k
│ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.srt& g" D# D6 e6 k2 ?
│ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.txt
7 F) H3 \4 ]; y J8 O9 y. K6 S) n │ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.mp4- F, k# P% g' J7 ~! w
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.srt
4 _/ t! a& d) G5 }' b8 r │ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.txt
/ J% G* Y* {& r6 T. h+ n │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.mp4
1 F$ v% m+ z4 e │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.srt
& M5 r+ X9 J, s% L │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.txt( Y C! B6 Z: g5 G2 r: ^- i# D& w' o
│ 6 - 12 - 12 - End of the Week - Duration 0021.mp4
1 [7 X- D$ l4 [. h0 x( n) r3 p) f │ 6 - 12 - 12 - End of the Week - Duration 0021.srt
6 ^; j4 ] j+ q7 P0 F, l9 x │ 6 - 12 - 12 - End of the Week - Duration 0021.txt
) {2 k/ C; [# g) e0 }3 o3 ?% y │ 6 - 2 - 2 - On Edges and Regions - Duration 0517.mp4
4 ]- L" ^7 @% D+ D0 ^7 ^; d │ 6 - 2 - 2 - On Edges and Regions - Duration 0517.srt# W4 ]* [+ t; g3 B# S
│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.txt- x( U( k7 E+ a' O& q5 p
│ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.mp4
' X. k$ I: L b* B- B │ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.srt: \& [$ P* I* X- h8 s: H
│ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.txt. J% b: u+ e6 _5 w' w
│ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.mp4
9 p% a7 f( f, d7 ` │ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.srt0 d. ~+ p+ q; B0 S* H" M. G
│ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.txt
7 d8 o7 K) e3 h3 Q1 x │ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.mp4
6 z1 b# g% ~" X │ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.srt {% C% H* S; L3 g' _
│ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.txt
" u4 k2 C* @1 W0 I/ v3 } │ 6 - 6 - 6 - Congratulations - Duration 0017.mp4, P, `1 L+ E( W0 G
│ 6 - 6 - 6 - Congratulations - Duration 0017.srt
% p3 b8 z |- T }& @ J. T( e │ 6 - 6 - 6 - Congratulations - Duration 0017.txt
+ n; z& K# }" `% w │ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.mp4/ G. w8 k- o k/ W* t+ S' {6 i0 x
│ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.srt
2 i1 B/ S ?( o$ B# J3 s" S5 t. B │ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.txt+ _9 z2 U9 d+ E: z# | A
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.mp43 H; s& m: T# @4 j5 E4 h
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.srt
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│ 6 - 9 - 9 - Mumford-Shah - Duration 0550.mp4
6 A% F& |$ K8 f$ [2 ?8 L │ 6 - 9 - 9 - Mumford-Shah - Duration 0550.srt
4 V6 u$ U. e( `8 \( w+ y │ 6 - 9 - 9 - Mumford-Shah - Duration 0550.txt, Y; n0 t% y9 k* b/ ]
│ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.mp4
3 n; y" C5 i: l# A1 n2 S │ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.srt3 L4 T1 S( @( _5 _* o& d& R
│ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.txt v5 \( G5 H! S; E3 V1 ?% d
│ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.mp4
4 n* b$ z: d( b │ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.srt) X' R/ x& z$ G5 q" J
│ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.txt& V" _( t+ Y4 J6 `
│ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.mp47 R5 [/ w! _' F2 b
│ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.srt2 U: b8 R' e1 x; z9 u
│ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.txt# w! I, f% u4 e( O0 U* I; J7 _4 z
│ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.mp4
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│ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.txt
. L. {# x) H0 E4 Z+ s6 l8 u5 t │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.mp4: O" q: [3 s) G [' J( l4 Q
│ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.srt
. S: b# ~5 P |& C; v │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.txt
o# U$ {0 g: y( S7 q │ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.mp4# C/ E+ V) `7 x/ D7 U. A9 ?! U2 K
│ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.srt2 c) H4 u( b p% u
│ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.txt, D/ Q0 h E" B1 r! w7 B( J
│ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.mp4
: G7 {3 w! r% N& A3 a2 t; G │ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.srt$ `3 c9 e# E# C6 E. a
│ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.txt! Y- F' {. {1 w4 y; m* p
│ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.mp4. j& F5 _) q! e0 j# p6 Z
│ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.srt
0 N3 c+ p" q1 o& N* Q& n │ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.txt, ^5 a i; K6 J. G
│ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.mp4
( z3 Y9 K+ Q2 K2 {' e5 X2 k │ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.srt
8 e3 }* z' n6 F/ I. F4 T │ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.txt
& d# K1 Y' W# ?+ ?% T4 n" \8 p │ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.mp4% _6 o: R# u6 s& b. T" j
│ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.srt
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% _3 J, o1 I; F) d1 F% \% T `" X │ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.mp4
* a- p7 ?% _) ^$ u │ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.srt
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│ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.mp4
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│ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.srt
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│ 8 - 6 - 6 - Demo - Photoshop Inpainting Healing Brush - Duration 0231.mp4" A" }* ~# f& V' w' M9 o
│ 8 - 6 - 6 - Demo - Photoshop Inpainting Healing Brush - Duration 0231.srt
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│ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039.mp4
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│ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039.txt |
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