* R, X0 s, R: |
├─001_machine_learning_by_andrew_ng_coursera4 X" z( s- \; W: ~7 W: y9 r; ^9 o% Z
│ └─001_machine_learning_by_andrew_ng_coursera. E( C' P% d5 R" D7 O" q5 B
│ ├─ml-003
' a$ q _' ]" ^8 h │ │ ├─01_I._Introduction4 p& q! H. J3 }
│ │ │ 01_Welcome.mp4
( {: W4 ~: J' T! j │ │ │ 01_Welcome.pdf+ p) ?/ l ?* n
│ │ │ 01_Welcome.pptx
J" O' X; b3 M6 i: z H │ │ │ 01_Welcome.srt
! @2 j9 @& ^7 {# _8 S* h │ │ │ 01_Welcome.txt$ E7 \* h7 i3 W8 a2 v
│ │ │ 02_What_is_Machine_Learning.mp4
2 P0 L: U" [) o' \ │ │ │ 02_What_is_Machine_Learning.srt
4 [( S% c+ M/ q; B( x7 r │ │ │ 02_What_is_Machine_Learning.txt4 j- [& K- j8 Q" I; q9 H" i1 p
│ │ │ 03_Supervised_Learning.mp4
+ m7 x. Y7 B0 q │ │ │ 03_Supervised_Learning.srt
( f: Q* x9 e; } │ │ │ 03_Supervised_Learning.txt
) m$ W* ~6 _6 A0 L. u# B' @) T │ │ │ 04_Unsupervised_Learning.mp44 \& x2 M" q& a4 i7 @
│ │ │ 04_Unsupervised_Learning.srt
e r* x& L/ h9 x7 a. i4 R$ E6 R6 x' I │ │ │ 04_Unsupervised_Learning.txt; A" {6 X' X$ ?
│ │ │
, k( b* E9 l% B, E0 O% h3 b │ │ ├─02_II._Linear_Regression_with_One_Variable
3 a7 M7 y$ a: _" D │ │ │ 02_Cost_Function.mp43 T5 W2 ^. u2 O' [
│ │ │ 02_Cost_Function.srt3 `7 s6 M( p1 B' W! i/ i+ _$ f- {
│ │ │ 02_Cost_Function.txt1 h* A! {+ \ p- X! q) i6 d
│ │ │ 05_Gradient_Descent.mp4
o# ?/ s1 |) @1 H8 I5 Y │ │ │ 05_Gradient_Descent.srt/ d% C% h5 v8 l7 q! \& `
│ │ │ 05_Gradient_Descent.txt
8 k5 C( c4 e3 f │ │ │ 08_Whats_Next.mp4
8 {. ?$ Y9 u0 ]! i. Y! M, _ │ │ │ 08_Whats_Next.srt
6 ]1 W9 t& i+ U% e: L │ │ │ 08_Whats_Next.txt* U# y" b6 v- o
│ │ │
Y7 z5 ]$ c' `9 m4 | │ │ ├─03_III._Linear_Algebra_Review, n8 `/ ~, b6 w/ g
│ │ │ 01_Matrices_and_Vectors.mp4
; l8 ]5 I; A/ ?5 t9 F6 w& } │ │ │ 01_Matrices_and_Vectors.pdf/ s% s" Z5 I* s+ p& m6 E9 J
│ │ │ 01_Matrices_and_Vectors.pptx$ I- ]/ q% C7 j
│ │ │ 01_Matrices_and_Vectors.srt
5 |9 q$ |- o& e, u9 a5 }1 y) o │ │ │ 01_Matrices_and_Vectors.txt
' r# T+ `9 B& H │ │ │ 03_Matrix_Vector_Multiplication.mp4
; o r0 S0 M0 O" _- { │ │ │ 03_Matrix_Vector_Multiplication.srt( X; O# p+ s" U+ t/ {. @7 O# u
│ │ │ 03_Matrix_Vector_Multiplication.txt
7 o. g) c; I- {% Y$ f8 L7 c, ~$ S" n3 c │ │ │ 04_Matrix_Matrix_Multiplication.mp4
, K! o0 ?$ B# X) ~ │ │ │ 04_Matrix_Matrix_Multiplication.srt
) ?0 U* Q! v A C1 d& m1 D& P │ │ │ 04_Matrix_Matrix_Multiplication.txt; N% C+ a R H
│ │ │ 05_Matrix_Multiplication_Properties.mp4
/ t3 x9 O! [7 ~" N' O+ a' F8 H │ │ │ 05_Matrix_Multiplication_Properties.srt% C+ g( K" p6 Q6 o- z
│ │ │ 05_Matrix_Multiplication_Properties.txt
9 B: {4 s2 @; i! i$ q9 i │ │ │ 06_Inverse_and_Transpose.mp4
5 @+ {8 z& ?! {" z. i' M │ │ │ 06_Inverse_and_Transpose.srt
% B) N5 N$ M: c9 @, r6 Y │ │ │ 06_Inverse_and_Transpose.txt
2 H$ m! |; d$ j- |9 p/ L( G │ │ │
* f* [( j6 P3 `/ g6 h │ │ ├─04_IV._Linear_Regression_with_Multiple_Variables
( t" v2 P" H' Z1 v) U# ?0 X7 [1 r │ │ ├─05_V._Octave_Tutorial
7 H6 M8 }( b* K, m2 X │ │ │ 01_Basic_Operations.mp4. r& u6 q) S7 D- C7 ?" C! P% O
│ │ │ 01_Basic_Operations.pdf
8 P! a6 V/ e9 V │ │ │ 01_Basic_Operations.pptx
4 y+ y1 ?8 |4 X3 u: ~0 k1 I) q │ │ │ 01_Basic_Operations.srt
4 [. a! h# ]! H ~ │ │ │ 01_Basic_Operations.txt$ g& I+ {5 l, o6 z k5 S \4 n
│ │ │ 02_Moving_Data_Around.mp4
& T$ q4 B v: e* _1 E T, D4 X │ │ │ 02_Moving_Data_Around.srt
. H: A1 x$ ^1 ?7 C; W │ │ │ 02_Moving_Data_Around.txt
8 E* `# W( X# J2 i: i │ │ │ 03_Computing_on_Data.mp4
) @% \- D3 `) ^2 r9 F5 |. Q │ │ │ 03_Computing_on_Data.srt! G1 p) @/ }2 {3 u9 {' b
│ │ │ 03_Computing_on_Data.txt
" y1 A2 V/ M9 w3 W7 W" w- {* T1 ] │ │ │ 04_Plotting_Data.mp4
9 v3 N' u: s% e7 {' L4 O6 W │ │ │ 04_Plotting_Data.srt
. H: n6 m) k+ l) Z/ z8 X# _ │ │ │ 04_Plotting_Data.txt6 c; K0 w n1 d5 j2 T
│ │ │ 06_Vectorization.mp41 N0 H" S3 t4 F. B4 r! M0 r0 p
│ │ │ 06_Vectorization.srt
& g# b+ n5 Y B( Q │ │ │ 06_Vectorization.txt# ^( [' O0 D$ X& n) D% {/ j, H
│ │ │ . J! e6 v% |/ E
│ │ ├─06_VI._Logistic_Regression7 d. N1 w/ C, e. V& s! A o
│ │ │ 01_Classification.mp46 @# g7 v l/ Y* U+ ^: j9 z
│ │ │ 01_Classification.pdf j) Q& z' V, O0 P- i% {% L
│ │ │ 01_Classification.pptx
! E- ]) y. D; l& s h │ │ │ 01_Classification.srt8 N& N3 W, m: d, k
│ │ │ 01_Classification.txt6 z# q9 X: D6 X6 X }$ \0 _
│ │ │ 02_Hypothesis_Representation.mp4
& `1 P9 }4 A! @7 T │ │ │ 02_Hypothesis_Representation.srt
, b$ \( Y# b2 C8 C4 Q │ │ │ 02_Hypothesis_Representation.txt# x' [0 _. A' X$ ^5 Y/ c
│ │ │ 03_Decision_Boundary.mp4
9 a' O8 F% `7 ?" p4 L. `( b% V │ │ │ 03_Decision_Boundary.srt
3 K$ r! @. a5 ^( B9 S9 L" l- ] │ │ │ 03_Decision_Boundary.txt
( P% B* H& T! x1 c2 g! x │ │ │ 04_Cost_Function.mp4% F& p) T* V3 O- |8 C8 P
│ │ │ 04_Cost_Function.srt
/ D/ q: z5 a. P8 G& } │ │ │ 04_Cost_Function.txt% a; Z e6 u E
│ │ │ 06_Advanced_Optimization.mp4
, }! d" n( ^' ?( |6 q │ │ │ 06_Advanced_Optimization.srt) a" Q/ g4 R4 n0 }; L
│ │ │ 06_Advanced_Optimization.txt
% O2 Z2 ^/ D. x1 v& I │ │ │ 6 [2 A% H5 x; a" W" g' B2 D* T
│ │ ├─07_VII._Regularization
9 k4 ~/ H" S+ T# z* i; ^% \3 ^7 ? │ │ │ 01_The_Problem_of_Overfitting.mp4
( s I+ [( p/ \6 I8 Y2 {) q │ │ │ 01_The_Problem_of_Overfitting.pdf
4 w+ }4 x, b0 J. Y' V: k6 D │ │ │ 01_The_Problem_of_Overfitting.pptx
& b2 J: i; ]$ h" z L0 e z │ │ │ 01_The_Problem_of_Overfitting.srt
4 x7 _0 `' Q. `. Y I% V │ │ │ 01_The_Problem_of_Overfitting.txt C( l! b# f+ G. C2 _. g
│ │ │ 02_Cost_Function.mp4( Z) w/ L. o2 a& x8 Y+ }& q, |: l& q
│ │ │ 02_Cost_Function.srt
1 k4 @6 V/ Y/ Z/ X8 y: ~! K) u& l6 t- _ │ │ │ 02_Cost_Function.txt
- T( K/ h2 n) E; N9 @( l$ r% R │ │ │ 03_Regularized_Linear_Regression.mp4
7 Q( c1 V* I: a( i, B3 K4 } │ │ │ 03_Regularized_Linear_Regression.srt
$ ~ ^# Z' Z/ u+ h% W │ │ │ 03_Regularized_Linear_Regression.txt2 {. @+ t( o J# e
│ │ │ 04_Regularized_Logistic_Regression.mp4
1 s$ L2 g7 K, k5 q( ]/ l2 U │ │ │ 04_Regularized_Logistic_Regression.srt5 y2 m: d' R e; j U
│ │ │ 04_Regularized_Logistic_Regression.txt
% v. A" w/ z; {* X" @ │ │ │
. G+ A/ d. _6 ?& e: U │ │ ├─08_VIII._Neural_Networks-_Representation
. Q: q# P! O3 R │ │ │ 01_Non-linear_Hypotheses.mp4* }/ V2 c& I# q' s; J" d4 h
│ │ │ 01_Non-linear_Hypotheses.pdf, v r# y2 N- O6 z& U6 {
│ │ │ 01_Non-linear_Hypotheses.srt3 _! u( _% N0 `# H+ }% R' X+ J
│ │ │ 01_Non-linear_Hypotheses.txt
( d. _8 i" o5 P2 O7 l │ │ │ 02_Neurons_and_the_Brain.mp4
7 F$ x' y* ^- J W: ]6 o │ │ │ 02_Neurons_and_the_Brain.srt
* ^* @7 J% h7 U │ │ │ 02_Neurons_and_the_Brain.txt; ^1 o( q1 m$ j: V+ F1 C* ^+ @
│ │ │
7 s- L7 U. Z( h# v0 `: D' G │ │ ├─09_IX._Neural_Networks-_Learning
( u* n9 Q" V4 G% B0 l │ │ │ 01_Cost_Function.mp4
, r- S2 L7 m# |% g- p; C5 W │ │ │ 01_Cost_Function.pdf
; l/ d) Z( @4 k, A$ E │ │ │ 01_Cost_Function.pptx
, T! E! I: M3 V* f K │ │ │ 01_Cost_Function.srt$ G7 a: s8 e/ M& h
│ │ │ 01_Cost_Function.txt1 a Y- f/ ^2 P- C9 W
│ │ │ 02_Backpropagation_Algorithm.mp49 o6 A8 K9 f: u; _- P' S
│ │ │ 02_Backpropagation_Algorithm.srt
; e) c6 {5 b* o7 H* x: g* Y │ │ │ 02_Backpropagation_Algorithm.txt; Q9 r. G# V' j( l
│ │ │ 03_Backpropagation_Intuition.mp4
5 c' f: p# G6 D! i- P, N2 j │ │ │ 03_Backpropagation_Intuition.srt: ~; a u2 J# N9 V5 }$ w
│ │ │ 03_Backpropagation_Intuition.txt
/ I9 T$ K6 Z# Q1 ~ │ │ │ 05_Gradient_Checking.mp4" V" S) @5 _5 w# f$ h& l8 d! \2 I- P
│ │ │ 05_Gradient_Checking.srt
6 S/ c5 m# z5 M! e# v, M0 e ? │ │ │ 05_Gradient_Checking.txt
~8 P2 a! R8 w8 J. M) k │ │ │ 06_Random_Initialization.mp4
( ] f& _# f& u6 }3 O1 G │ │ │ 06_Random_Initialization.srt
3 C3 D) Q( w: K │ │ │ 06_Random_Initialization.txt* r2 c) K2 }6 y. a* Q, G
│ │ │ 07_Putting_It_Together.mp43 T. {: B! H; w1 _4 W5 c. K8 K) S
│ │ │ 07_Putting_It_Together.srt' ^3 N Z2 T- C' \: T: @4 G
│ │ │ 07_Putting_It_Together.txt
: J' s) I1 {6 v( n# z │ │ │ 08_Autonomous_Driving.mp49 @2 x k4 [% `% p
│ │ │ 08_Autonomous_Driving.srt
- X: [1 z0 C- H) g# T7 a8 ~ │ │ │ 08_Autonomous_Driving.txt
( _$ h T3 ?' ^ │ │ │
, q+ ` H1 r1 H# R │ │ ├─10_X._Advice_for_Applying_Machine_Learning! O' { o. R. q" w) x' K8 g
│ │ │ 06_Learning_Curves.mp4
/ `/ x7 H* S! c! P6 S │ │ │ 06_Learning_Curves.srt) o1 z5 f- B! v2 g
│ │ │ 06_Learning_Curves.txt
' R* Y2 [4 Z% l │ │ │
g* F! O' s) a) `" D; ^ H │ │ ├─11_XI._Machine_Learning_System_Design
3 o0 y @0 N% p │ │ │ 02_Error_Analysis.mp4# Q: s1 }( @* C- M& t X+ E* I
│ │ │ 02_Error_Analysis.srt* c" V) f7 o+ f
│ │ │ 02_Error_Analysis.txt; k" J$ y3 S* `$ C4 S( E
│ │ │ ' ^$ E- Y3 X% h; o
│ │ ├─12_XII._Support_Vector_Machines
: ]! W+ Q$ E3 D' F- m │ │ │ 01_Optimization_Objective.mp4: l8 m! O$ q' i. ?& V
│ │ │ 01_Optimization_Objective.pdf
/ r# j, e, R4 x- F$ ? │ │ │ 01_Optimization_Objective.pptx' c9 y$ w Z$ |
│ │ │ 01_Optimization_Objective.srt6 P3 t8 k; O/ b/ w& S/ b+ J0 Y
│ │ │ 01_Optimization_Objective.txt' z$ O m6 Q; l* r, i! J
│ │ │ 02_Large_Margin_Intuition.mp4
R( H V) ^; e: W │ │ │ 02_Large_Margin_Intuition.srt
. w7 ?5 s9 ?4 z8 J- U7 ^ │ │ │ 02_Large_Margin_Intuition.txt) Q/ d4 F$ R; O! y
│ │ │ 04_Kernels_I.mp4
* `+ F( J0 h. ?2 l │ │ │ 04_Kernels_I.srt
* Q( K% K* T( H- c$ m │ │ │ 04_Kernels_I.txt7 i5 N( k0 K% G: c) C
│ │ │ 05_Kernels_II.mp4
6 d2 y& z% h6 C) `4 Q │ │ │ 05_Kernels_II.srt
& f3 @6 F9 U& X6 Z+ B6 _1 s │ │ │ 05_Kernels_II.txt
7 N9 J- f5 t8 V4 J8 f" I │ │ │ 06_Using_An_SVM.mp4
* z, `- L" A H" w( f0 z7 m% s │ │ │ 06_Using_An_SVM.srt$ N; b; o& B* z; W
│ │ │ 06_Using_An_SVM.txt
; y& E `0 h. _6 D9 Q0 w5 o │ │ │ ! {- v8 x: K( P- R* f: m" E9 m
│ │ ├─13_XIII._Clustering
! q n U# @0 g. V2 H3 O( Y$ S │ │ │ 01_Unsupervised_Learning-_Introduction.mp4
4 ?; g: Z U% G7 c7 ~1 O │ │ │ 01_Unsupervised_Learning-_Introduction.pdf
5 d y+ W* h- l0 M. ` │ │ │ 01_Unsupervised_Learning-_Introduction.pptx0 l" F2 `3 ]$ N0 G* k, L% C* t3 {. N
│ │ │ 01_Unsupervised_Learning-_Introduction.srt4 h% S( B7 Y6 x/ o5 x) r. T
│ │ │ 01_Unsupervised_Learning-_Introduction.txt
" E! [+ N. B% X$ O0 z$ n │ │ │ 02_K-Means_Algorithm.mp4! D& u; ^. b. ^. A; Y$ Q- M# D8 X# R
│ │ │ 02_K-Means_Algorithm.srt
: [8 O9 _) m( c3 b& ? │ │ │ 02_K-Means_Algorithm.txt
2 T" Q0 \- r; u0 P% f │ │ │ 03_Optimization_Objective.mp4
6 j3 R7 y* v' Y- Z │ │ │ 03_Optimization_Objective.srt
- p2 g) G1 O, E" U# E │ │ │ 03_Optimization_Objective.txt( j& Q$ ~" O7 G+ ~( w4 a4 w9 A0 u, X
│ │ │ 04_Random_Initialization.mp4
/ f! L8 I! a$ |' J* Y8 V │ │ │ 04_Random_Initialization.srt0 }* R' {" ]% s6 o: q' m( ]
│ │ │ 04_Random_Initialization.txt
9 m2 v }, L, a, \ │ │ │ 05_Choosing_the_Number_of_Clusters.mp4
. G+ k1 J+ J) |% w1 J3 M │ │ │ 05_Choosing_the_Number_of_Clusters.srt
7 L, V2 O" L: r5 q/ S$ A3 X │ │ │ 05_Choosing_the_Number_of_Clusters.txt# ?! }: L4 ~! m- j; ?) u
│ │ │ 2 L) s$ W. q" o P( t+ D, L% R6 W
│ │ ├─14_XIV._Dimensionality_Reduction) A" B. n# T( H8 L" g, T) ^* t
│ │ │ 02_Motivation_II-_Visualization.mp4
2 x) m) [9 b" v" y' X │ │ │ 02_Motivation_II-_Visualization.srt y7 E$ y6 W: t% _- Q
│ │ │ 02_Motivation_II-_Visualization.txt
6 A- T. g- y: l& d2 A │ │ │ 07_Advice_for_Applying_PCA.mp4# z; ^& b/ n) X3 s% V ]
│ │ │ 07_Advice_for_Applying_PCA.srt
: [$ Q* ~- g0 l7 e7 [+ k │ │ │ 07_Advice_for_Applying_PCA.txt! e; V9 O' S2 @! U/ \: m
│ │ │ ' s: [% o2 T1 H& v7 A( n& [$ e
│ │ ├─15_XV._Anomaly_Detection
" v5 i l" Z& a- q$ g │ │ │ 01_Problem_Motivation.mp4/ D6 g$ u2 D1 y+ Y# V+ i7 b
│ │ │ 01_Problem_Motivation.pdf
: |4 e9 R% x i# \( X# Y │ │ │ 01_Problem_Motivation.pptx
, g; l; {. e( U. p8 x! u │ │ │ 01_Problem_Motivation.srt2 E! o1 K8 x P
│ │ │ 01_Problem_Motivation.txt+ f1 \8 b2 c" D: {: d% ] P4 f4 u
│ │ │ 02_Gaussian_Distribution.mp4
# s6 c. b- |8 g* `* u) U │ │ │ 02_Gaussian_Distribution.srt
! B' K6 v9 A" ~1 D. Q │ │ │ 02_Gaussian_Distribution.txt8 |' ] D$ I3 d% a8 @. ` A" x
│ │ │ 03_Algorithm.mp4
8 `9 `2 I9 h7 K7 b8 H │ │ │ 03_Algorithm.srt
/ ~- g1 J8 k1 ` │ │ │ 03_Algorithm.txt) \2 ] ]+ i7 ?8 f
│ │ │ 06_Choosing_What_Features_to_Use.mp48 O3 L- o; ^: B7 E( z, H- x: p
│ │ │ 06_Choosing_What_Features_to_Use.srt# t, W) o- C# I
│ │ │ 06_Choosing_What_Features_to_Use.txt
2 j( e1 j9 d' @( B: P& W │ │ │
/ \9 {$ n1 W: f$ a │ │ ├─16_XVI._Recommender_Systems
1 h3 D0 U" Q8 b0 ]0 {" [; ]9 C │ │ │ 01_Problem_Formulation.mp4; b7 E) m0 J- p
│ │ │ 01_Problem_Formulation.pdf
$ t g, z6 L( Q, s% H+ r9 f5 @; H$ z; J. T │ │ │ 01_Problem_Formulation.pptx
; H. U3 X) I# t │ │ │ 01_Problem_Formulation.srt8 P, b/ \" r4 J, d/ Z
│ │ │ 01_Problem_Formulation.txt
" y I9 S: t! X1 F │ │ │ 02_Content_Based_Recommendations.mp4
8 t4 b; W% d- p- e# o: a I- ~ │ │ │ 02_Content_Based_Recommendations.srt8 o" `- |* e. B. H# h
│ │ │ 02_Content_Based_Recommendations.txt/ y" F, ~6 p; q8 \ k
│ │ │ 03_Collaborative_Filtering.mp4+ F/ X! c/ L. z+ V! |5 X- _
│ │ │ 03_Collaborative_Filtering.srt
/ R9 V4 [' O: y3 r5 W4 N p │ │ │ 03_Collaborative_Filtering.txt
1 T( s' a6 ~1 p; [1 | │ │ │ 04_Collaborative_Filtering_Algorithm.mp4( ]' Z i' Z0 Q! S& q9 T# f
│ │ │ 04_Collaborative_Filtering_Algorithm.srt
; ~% ]9 U4 d% | │ │ │ 04_Collaborative_Filtering_Algorithm.txt
: S3 A" @0 y& H6 O- }! O! p │ │ │
" l1 b8 U0 G. G+ R! U6 z% f3 W │ │ ├─17_XVII._Large_Scale_Machine_Learning) E9 W5 q2 }) T, p
│ │ │ 05_Online_Learning.mp43 N+ t0 J- {0 D! S
│ │ │ 05_Online_Learning.srt- {/ R9 K* I& Q. q3 U( X# ^
│ │ │ 05_Online_Learning.txt w6 N/ x f+ z" o$ b
│ │ │
7 F: e" l+ e6 F+ X │ │ ├─18_XVIII._Application_Example-_Photo_OCR( b9 X7 `! l( g+ K0 ~- K
│ │ │ 02_Sliding_Windows.mp4
% Q0 ?3 D6 b# J- J2 M% K │ │ │ 02_Sliding_Windows.srt
6 x/ [1 B8 {* a5 v+ f │ │ │ 02_Sliding_Windows.txt! O( a% P+ \" u2 b( j' i3 n
│ │ │
6 D# d }% F' X3 G3 W │ │ └─19_XIX._Conclusion' D; x& Q! o8 V! @7 _: C7 Q1 `
│ │ 01_Summary_and_Thank_You.mp46 O! \" o3 Q( T6 h' K( T
│ │ 01_Summary_and_Thank_You.srt/ V' X+ W: G6 Z8 {0 Q
│ │ 01_Summary_and_Thank_You.txt/ K- L/ z5 p2 W
│ │ / [& R" x4 h; ] D" V
│ └─机器学习-斯坦福-Andrew NG-2012
. m" W1 K: T- b p( H │ │ 下载说明.txt
- ]% F0 K8 O$ ]( h, ^ │ │ 关注我们.png) F! b* ^5 i j- }6 \" x( V% h
│ │ 攻城狮论坛=网络技术+编程视频.url
! W5 x( y+ d5 }3 x │ │ 机器学习笔记2012_v0.1.pdf
5 t" z) R& T# j0 I( Z │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt' L: I( T% K8 Y6 a: ]3 q4 d0 K
│ │ + v, p" `" q4 _* e% v# b$ j- X
│ ├─week 1
. i3 ?1 q! Y. G$ i7 j; b │ │ 1 - 1 - Welcome (7 min).mp4
, G4 a! N$ R* v% A3 o$ \- ` │ │ 1 - 1 - Welcome (7 min).srt
3 N6 t3 T8 F, u: F9 m │ │ 1 - 2 - What is Machine Learning (7 min).mp45 U7 ^- k+ B+ q/ |
│ │ 1 - 2 - What is Machine Learning- (7 min).srt( C" {8 h& U( {2 J( N: D6 K( S# J
│ │ 1 - 3 - Supervised Learning (12 min).mp4
) x! \. Y5 D/ i │ │ 1 - 3 - Supervised Learning (12 min).srt V( @1 I6 } q/ Q, e
│ │ 1 - 4 - Unsupervised Learning (14 min).mp4
0 p) X! n# b( p! X& V │ │ 1 - 4 - Unsupervised Learning (14 min).srt
7 `% Y' Q4 u2 ^% d r9 D" ~* R6 E │ │ 2 - 1 - Model Representation (8 min).mp4) j6 I& v6 L) ?& X
│ │ 2 - 1 - Model Representation (8 min).srt: g) k' Z7 a u* y& e% \
│ │ 2 - 2 - Cost Function (8 min).mp4. y$ ^) x. ~4 e9 V1 u% N8 V# b+ X
│ │ 2 - 2 - Cost Function (8 min).srt9 S# S) u% I+ W |' C5 b
│ │ 2 - 5 - Gradient Descent (11 min).mp4
8 m& Y2 I+ ]0 o │ │ 2 - 5 - Gradient Descent (11 min).srt; K6 M3 a% S; J# v! t4 E
│ │ 2 - 8 - What's Next (6 min).srt) ?4 E7 h+ t2 Y2 C+ a0 G! c
│ │ 2 - 8 - Whats Next (6 min).mp4
) t7 u: R2 M: v R+ M$ q7 D │ │ 3 - 1 - Matrices and Vectors (9 min).mp41 p p5 n Q1 P
│ │ 3 - 1 - Matrices and Vectors (9 min).srt9 T* a1 F2 s- c \
│ │
7 A, _& t" C! x6 n: G │ ├─week 103 Q& \" M j, V
│ │ 17 - 5 - Online Learning (13 min).mp42 {4 T8 {( B# N: ?* T0 i# t
│ │ 17 - 5 - Online Learning (13 min).srt
6 `9 @& ~9 n; S! Q* v+ ~/ c: z │ │ Lecture17.pdf
- L( {8 ^* g6 g. d2 H │ │
1 u/ t8 P* Z0 T3 R1 n │ ├─week 2
8 c d$ ]! O) J+ G& f │ │ 4 - 1 - Multiple Features (8 min).mp4! t3 T5 t5 C+ {8 c: I& Z. b
│ │ 4 - 1 - Multiple Features (8 min).srt
+ I* E0 ^- z4 z5 R/ D! r% ^ │ │ 4 - 6 - Normal Equation (16 min).mp4
- N6 A8 Y( }. m# Y │ │ 4 - 6 - Normal Equation (16 min).srt! @' n* V) h% {2 H3 o& V3 d2 ^
│ │ 5 - 1 - Basic Operations (14 min).mp48 B; m# i: z8 b7 n8 f$ g
│ │ 5 - 1 - Basic Operations (14 min).srt7 L* V2 x5 k$ p5 r
│ │ 5 - 2 - Moving Data Around (16 min).mp4
6 T$ B* v. P; B8 c) V │ │ 5 - 2 - Moving Data Around (16 min).srt
* H+ }$ w4 U. n" S* D4 M4 @& A │ │ 5 - 3 - Computing on Data (13 min).mp4+ K, N' W- E; O) V' G2 T' G
│ │ 5 - 3 - Computing on Data (13 min).srt0 a+ G9 S0 B# G- t
│ │ 5 - 4 - Plotting Data (10 min).mp4
8 q- N' D& A5 k4 x. D; p, K, N │ │ 5 - 4 - Plotting Data (10 min).srt1 a: I' G. J7 e8 J
│ │ 5 - 6 - Vectorization (14 min).mp4% D6 H; W) k2 ]2 e" m+ i; \
│ │ 5 - 6 - Vectorization (14 min).srt- U Q# \# y$ d2 E- W
│ │ Lecture4.pdf% s5 H3 E9 p1 U7 T$ N
│ │ Lecture5_octave tutorial.pdf# X7 w- o1 S. @3 @
│ │
1 R; `9 ^6 v( ?7 h H f( s │ ├─week 3" o; J1 q, D2 e1 B
│ │ 6 - 1 - Classification (8 min).mp4
3 S8 [1 q( N, A │ │ 6 - 1 - Classification (8 min).srt0 j1 G! R' F7 u! m+ T
│ │ 6 - 2 - Hypothesis Representation (7 min).mp4
E7 E$ O+ V, |$ f │ │ 6 - 2 - Hypothesis Representation (7 min).srt! X6 |0 K. \% B
│ │ 6 - 3 - Decision Boundary (15 min).mp4
# h: o0 q2 P7 y8 N8 w │ │ 6 - 3 - Decision Boundary (15 min).srt. ~! C# _; @1 q. ]
│ │ 6 - 4 - Cost Function (11 min).mp4
! W7 V( M+ w7 n% ~ │ │ 6 - 4 - Cost Function (11 min).srt
* q( h2 @6 g; Q9 N( N' M* v: |4 Q( \% o$ { │ │ 6 - 6 - Advanced Optimization (14 min).mp46 `% g/ W) `1 ^# `8 {
│ │ 6 - 6 - Advanced Optimization (14 min).srt* x7 ~; n6 _4 ^# z! \. }* p7 t
│ │ 7 - 2 - Cost Function (10 min).mp4/ @3 L1 Q5 {* p; O" W" B1 f1 p
│ │ 7 - 2 - Cost Function (10 min).srt8 V( P6 t# C+ v7 d4 j. u5 K
│ │ docs_slides_Lecture6.pdf
9 K5 h6 n' n5 Y8 p+ r) z9 |0 M │ │ docs_slides_Lecture7.pdf' G5 f. u8 P# d4 g7 t
│ │
# y5 R6 a! k2 v1 X8 x0 J │ ├─week 4
7 a1 b+ R0 Y6 ~3 V* W- r4 }. F5 z │ │ 8 - 1 - Non-linear Hypotheses (10 min).mp4& w3 O# C4 Q0 e- M% _- a# D$ x
│ │ 8 - 1 - Non-linear Hypotheses (10 min).srt
& S8 N1 B6 t& ~2 E │ │ 8 - 2 - Neurons and the Brain (8 min).mp4
7 N3 c0 a$ p" q& F# a: { │ │ 8 - 2 - Neurons and the Brain (8 min).srt
/ } Q' D0 ?6 b6 L$ S5 X: s; a: N │ │ 8 - 3 - Model Representation I (12 min).mp44 A5 K8 w% b2 |9 o' R
│ │ 8 - 3 - Model Representation I (12 min).srt
3 i8 z; C; B2 H │ │ 8 - 4 - Model Representation II (12 min).mp4
. a) r7 {7 t, }4 y4 ]' ] │ │ 8 - 4 - Model Representation II (12 min).srt* D8 R5 U1 W" F/ U7 x# _! T# P) i
│ │ 8 - 5 - Examples and Intuitions I (7 min).mp40 f7 X% V/ ^1 [6 a; s
│ │ 8 - 5 - Examples and Intuitions I (7 min).srt
5 ]" k9 m, O% r u0 f" Z m │ │ 8 - 7 - Multiclass Classification (4 min).mp4
% U! w# e& k8 m. b- d2 z/ j# L$ ` │ │ 8 - 7 - Multiclass Classification (4 min).srt
; N0 v, \5 m" k- h# [- C, ~; f7 f │ │ docs_slides_Lecture8.pdf
) _- a5 N' p8 n$ H8 x$ ] │ │ : n$ X1 q* E( S1 e) o, n: C
│ ├─week 5; e+ G8 ~3 e1 [+ |; ~2 m
│ │ 9 - 1 - Cost Function (7 min).mp42 l( C) L5 T) c! v' R
│ │ 9 - 1 - Cost Function (7 min).srt
/ G7 Y) B0 }, p' W │ │ 9 - 5 - Gradient Checking (12 min).mp46 _1 }8 U( a! x3 b- C, Y" z
│ │ 9 - 5 - Gradient Checking (12 min).srt x& y% p8 Y# C- W) d7 ?3 h
│ │ 9 - 6 - Random Initialization (7 min).mp43 @/ S: k% j- \$ X2 a1 \8 J$ o
│ │ 9 - 6 - Random Initialization (7 min).srt
, Q0 N) E% k. k* F& O* J$ P │ │ 9 - 7 - Putting It Together (14 min).mp4" z$ I1 ^7 m% f0 h3 i
│ │ 9 - 7 - Putting It Together (14 min).srt! u: S0 N9 ]/ J0 j' }9 E
│ │ 9 - 8 - Autonomous Driving (7 min).mp4
" D7 c, t- V4 S3 m3 C3 o. W1 d9 U │ │ 9 - 8 - Autonomous Driving (7 min).srt
4 [- h6 q7 s2 Q/ a6 p( \ │ │ docs_slides_Lecture9.pdf
s: F9 H& M7 J# N7 L G │ │
# A# J4 x: ?( g8 k. g! ~ │ ├─week 6$ H% a& w1 y/ }9 w, W9 i; g# j
│ │ 10 - 2 - Evaluating a Hypothesis (8 min).mp4: S" l {6 f W* \# B- j) s; h
│ │ 10 - 2 - Evaluating a Hypothesis (8 min).srt
/ w4 h: K& |0 u% I │ │ 10 - 6 - Learning Curves (12 min).mp4. \8 `- W( [3 M3 a/ K
│ │ 10 - 6 - Learning Curves (12 min).srt6 [- U. o5 [: b( x
│ │ 11 - 2 - Error Analysis (13 min).mp4
9 K5 u( S9 t# k8 `% f5 @9 | │ │ 11 - 2 - Error Analysis (13 min).srt; f- L0 f8 f5 a5 P% W
│ │ docs_slides_Lecture10.pdf" ^* Y3 Q8 h+ o/ y
│ │ * {! M1 N2 ~& [, d; I* G
│ ├─week 7' v: S" L4 Y5 D
│ │ 12 - 1 - Optimization Objective (15 min).mp4
# m7 A1 a. S2 D+ f │ │ 12 - 1 - Optimization Objective (15 min).srt
7 W/ y8 l2 a' R. x0 m │ │ 12 - 2 - Large Margin Intuition (11 min).mp48 { {0 Y( [0 A! ^* m
│ │ 12 - 2 - Large Margin Intuition (11 min).srt+ j, Y% v- ?- L2 q8 L8 E* k
│ │ 12 - 4 - Kernels I (16 min).mp44 B# } W4 N7 T* S
│ │ 12 - 4 - Kernels I (16 min).srt
% r7 q' `. p# J) ? │ │ 12 - 5 - Kernels II (16 min).mp4
! m, D! } P9 f1 q; G/ y" n │ │ 12 - 5 - Kernels II (16 min).srt/ t( F. R' P' p$ z/ o+ Z7 l5 j
│ │ 12 - 6 - Using An SVM (21 min).mp4- w7 u) j7 ]8 A+ f7 w5 {" X
│ │ 12 - 6 - Using An SVM (21 min).srt9 C3 o, ]* v! J/ m$ i0 p
│ │ docs_slides_Lecture12.pdf
8 N2 T1 {% G( F* V6 C$ U │ │
4 T1 d z) q. S) [- P │ ├─week 8! A9 \6 t/ H0 G- M9 |
│ │ 13 - 2 - K-Means Algorithm (13 min).mp4
! M+ a D. n, F2 t1 W │ │ 13 - 2 - K-Means Algorithm (13 min).srt8 E( W9 w( v/ _$ J1 o
│ │ 13 - 3 - Optimization Objective (7 min).mp4! _- m% `7 _- C! J
│ │ 13 - 3 - Optimization Objective (7 min).srt
" Y6 ^$ w+ L! ?6 i1 P' ?3 k0 J' B │ │ 13 - 4 - Random Initialization (8 min).mp4
, W) V/ k9 B) L │ │ 13 - 4 - Random Initialization (8 min).srt" T# P; e c; {7 _( P, F/ W: L1 A
│ │ 14 - 7 - Advice for Applying PCA (13 min).mp4. y1 O8 }: X& E" d- h% S1 N
│ │ 14 - 7 - Advice for Applying PCA (13 min).srt7 Y0 X& B$ G4 d% S4 V
│ │ docs_slides_Lecture13.pdf' c/ |2 }2 g' Z
│ │ docs_slides_Lecture14.pdf: A+ B4 M9 O& F8 x6 T) \2 W
│ │ ( g; j1 [. B6 ~8 ~) c- F$ D
│ ├─week 9
% U8 @/ f: J f$ ?2 [1 ^ │ │ 15 - 1 - Problem Motivation (8 min).mp4
* t) W; h/ e! ?0 }0 W# D │ │ 15 - 1 - Problem Motivation (8 min).srt
3 J) L0 n1 t1 c) J6 Y3 V: a4 K; k: Q( F │ │ 15 - 2 - Gaussian Distribution (10 min).mp4
8 |7 {- w- y% s) s. O" z$ {/ F │ │ 15 - 2 - Gaussian Distribution (10 min).srt' {7 l9 q& |7 r" k1 O$ K, e
│ │ 15 - 3 - Algorithm (12 min).mp4( p1 D7 n) `7 @" |6 g. w: W0 K
│ │ 15 - 3 - Algorithm (12 min).srt$ h- }6 c0 d& u" M& a9 g5 q
│ │ 16 - 1 - Problem Formulation (8 min).mp4
+ E2 o" o& x( y7 ?* J& J │ │ 16 - 1 - Problem Formulation (8 min).srt
, v. L( {9 u; A2 [ │ │ 16 - 3 - Collaborative Filtering (10 min).mp4
2 ]7 d! F9 a3 y* y │ │ 16 - 3 - Collaborative Filtering (10 min).srt9 H: {& c- ~* L. f2 ^
│ │ docs_slides_Lecture15.pdf( b% `1 R. h! ]+ a# y
│ │ docs_slides_Lecture16.pdf
& Z/ z" e6 @* N5 c- H │ │
( C* K; w4 N7 S. R! j* ^$ ^9 G │ └─作业4 f! x, Z* B! @& j0 H4 B
│ │ ex1.zip2 r7 N$ z( I2 F- s7 U
│ │ ex3.zip
& b4 f& D1 D/ f, v! J │ │ ex4.zip3 c1 T* Q" Z8 T& Z, G3 v9 l$ E
│ │ ex5.zip0 q: f$ U6 k7 o# v8 z) p" S2 U
│ │ ex6.zip
- g- A/ ]; m3 m0 c; i │ │ ex7.zip, C3 }6 t' t* c! a y0 j5 V
│ │ ex8.zip U* x' x* o \! L- q) l) f. o% c
│ │ Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe
! F `7 F) _. ]8 n7 S& L │ │
- m1 y9 J1 L. w% {! m+ F4 n │ └─答案
3 e; M# U. f' H7 J4 m │ ├─ex1
/ r+ P% ~; p1 S* v) s$ |7 J8 ^0 N0 ` │ │ computeCost.m7 q' ~% w9 P% u- u M/ m E& p" T
│ │ computeCostMulti.m
' P6 U( _( r* N% }0 e, G! h4 l │ │ ex1.m- @9 S, J# P) e4 }
│ │ ex1data1.txt
' n! [( T3 |/ Y" z' q* U │ │ ex1data2.txt
$ m4 H6 u8 O% N& |- o │ │ ex1_multi.m
* a+ q6 \7 X7 k$ _5 o) j" X │ │ featureNormalize.m
, \/ h2 v/ o6 G3 k) |. J │ │ gradientDescent.m
0 Z0 N6 Z) q( Q P: z. l& d │ │ gradientDescentMulti.m! M+ q/ o; W. u
│ │ ml_login_data.mat
% d6 L6 d# Y- x' y: G) S: b/ C │ │ normalEqn.m
! T; t& N- n; A6 v │ │ plotData.m
& h$ X! Z* s$ h, [+ U" e. N │ │ submit.m$ J/ ]& B6 D6 V5 s9 t0 Q5 }
│ │ submitWeb.m6 ^% c) L7 |7 F
│ │ warmUpExercise.m# K. N4 Y, H# q
│ │
8 w. s( g7 Q" ^. p7 k u │ ├─ex2
% ~4 n9 o/ n/ s, U7 S │ │ costFunction.m8 F: ]8 \$ A* r$ d& R; ^- I
│ │ costFunctionReg.m4 e% N! I' y& n2 u, n
│ │ ex2.m# G3 h) ` W: l& j0 B4 }
│ │ ex2data1.txt
* U8 Z- P( J9 O! ?( j& e# h │ │ ex2data2.txt
2 V1 G$ a" r9 E+ _ │ │ ex2_reg.m
% h9 |, e q5 E6 v6 |# a │ │ mapFeature.m7 J- }3 f- u3 G1 L) o
│ │ ml_login_data.mat+ y9 C8 L7 J' M0 ^4 R O
│ │ plotData.m" i% e" w9 W. a: `( G
│ │ plotDecisionBoundary.m/ s- D# f# O2 S4 K/ a' @( W% A0 I
│ │ predict(1).m.baiduyun.downloading9 M) ?- G( y3 e9 G5 v& d
│ │ predict.m- N* {" b* i. @5 X% j! Y
│ │ sigmoid.m/ Z, k4 J- F1 G" }) U
│ │ submit(1).m.baiduyun.downloading
+ B @2 M& u0 _' D; l6 ~ │ │ submit.m" P& l* v# ^1 O5 v# S' y7 j4 ~
│ │ submitWeb(1).m.baiduyun.downloading. @( b4 o" B6 W& J
│ │ submitWeb.m1 R* i# s" U/ @- E
│ │ 9 C% D3 [! M0 C6 p; F2 R
│ ├─ex3
0 ?7 N4 f, M! c1 F- }1 s4 S │ │ displayData.m
6 S% g# w" A; \# ` │ │ ex3(437).m.baiduyun.downloading) |1 v v( i" F4 R: r$ I6 Q! j
│ │ ex3.m: |% C0 h% ~$ m. u) x5 n% Y
│ │ ex3data1.mat* P' T, z. f- b5 S+ @) c
│ │ ex3weights.mat
$ D1 @- y9 H Z$ g! t │ │ ex3_nn.m
3 v+ l" }9 g$ x. q/ }$ d │ │ fmincg.m$ d; }7 w8 V0 a
│ │ lrCostFunction.m
o. l$ Z* A, j/ c │ │ ml_login_data.mat
7 r& [1 @ w" f, q2 x6 H │ │ oneVsAll.m! i# [. W4 ], Y9 H ^- ?: c6 Y
│ │ predict.m
" t) }) V& G& i │ │ predictOneVsAll.m
! h" W' c. p0 m4 G │ │ sigmoid.m
* G' h3 i9 W, F4 j8 U2 v5 P& | │ │ submit.m
, x& E4 P0 G- ^* j1 Q │ │ submitWeb.m( K1 B o- ?' G+ C4 O9 }: p/ _0 H
│ │ , K5 T5 L; U h
│ ├─ex4
, T* y: G7 M: q J- a! J e6 i6 Z │ │ checkNNGradients.m- o+ k7 a1 y9 o7 A. k5 d
│ │ computeNumericalGradient.m8 _: C0 O4 J' ]8 U; p# I
│ │ debugInitializeWeights.m" M' W. G3 b+ {0 y
│ │ displayData.m
! v9 ]0 T& w/ T! a │ │ ex4.m
; W& c& r- n: K+ q7 e │ │ ex4data1.mat( g) o* X4 v! `* p+ z/ B
│ │ ex4weights.mat
4 m8 t4 P9 U. z# _. V1 Q8 o. f& T │ │ fmincg.m) C0 l' L" y& z5 @
│ │ ml_login_data.mat
$ f9 d' y$ d$ @& |+ f │ │ nnCostFunction.m
0 U5 g% m: X& [4 ^ │ │ predict.m% U( b5 B" G9 m: y! K
│ │ randInitializeWeights.m
8 {, O# N" \- N# c │ │ sigmoid.m
: d9 _ ]5 I1 C+ n8 d │ │ sigmoidGradient.m. R) L; q2 w; a/ l
│ │ submit.m
0 T0 A- t. n/ B │ │ submitWeb.m
% } b o( q0 F* p3 t │ │
! ]* C# b" J* w; o# [ │ ├─ex5$ T: Z3 {4 n2 F2 s$ y" a
│ │ ex5.m4 b( f- _% X/ V& b6 B7 I, y8 {
│ │ ex5data1.mat
: s6 r$ R- I8 z, M, k │ │ featureNormalize.m/ L4 N3 y f% ]; J8 Z1 T( y
│ │ fmincg.m
$ [$ n& K! w1 X2 Z& G9 i0 R- K │ │ learningCurve.m$ |* X# Q* k1 H
│ │ linearRegCostFunction.m4 t/ W) u2 {7 Y. n! U' l
│ │ ml_login_data.mat
) Y/ X$ {) J; ~) f: @ ^( G │ │ plotFit.m6 G& C8 w' T d" E% w8 Q9 ?* V" u, o
│ │ polyFeatures.m
6 |1 D5 [* T) C) |* |+ ^* l. x9 n │ │ submit.m: [ P! |6 q. E6 @3 z
│ │ submitWeb.m! v7 y$ y+ @- h! p/ `' i* @) q, W# Z
│ │ trainLinearReg.m: d; A8 B) a/ O) d5 p
│ │ validationCurve.m/ r; _ t/ E- u5 q
│ │
7 ]1 ~; V: [1 k9 [, A" ` │ ├─ex6 T: u; j& M2 b
│ │ dataset3Params.m' z) `/ H, O4 F& \4 m/ Y
│ │ emailFeatures.m
; _; t! p$ q9 p9 z) l* t" ` │ │ emailSample1.txt3 |0 I- g6 m' G D8 g k
│ │ emailSample2.txt* A. }' w" U/ ~' @ \, n
│ │ ex6.m2 D# X$ z) ~8 y6 U
│ │ ex6data1.mat
! R: q. j% ]3 g+ l │ │ ex6data2.mat
8 I5 |4 b' ~5 Y% J* `: ? │ │ ex6data3.mat$ o6 A1 ~1 p6 W; \2 F1 o/ l
│ │ ex6_spam.m
# O+ Z( M, p* e5 b. X3 x$ I │ │ gaussianKernel.m. G* ]. z5 e' d3 y
│ │ getVocabList.m" l" U- Q1 z. X. O$ u7 n
│ │ linearKernel.m" A5 S& U& Z. F& Y" w
│ │ ml_login_data.mat4 v: x# M' }: Y N
│ │ octave-core0 o0 b4 B8 a: i1 k. b2 i1 ?
│ │ plotData.m8 t" e- J/ B" X7 A3 v+ A
│ │ porterStemmer.m0 W) N; ]+ K: _2 g" N/ |0 [( n
│ │ processEmail.m" c& |6 P9 Y, H9 D. Z% t" b5 |9 J
│ │ readFile.m! u( o4 h; G7 n( i5 u# ^% L
│ │ spamSample1.txt7 _4 d1 ^' j; s- t X& q
│ │ spamSample2.txt' _' f& A! `! ~+ m; U& K
│ │ spamTest.mat
6 z, w8 N* w1 k7 S │ │ spamTrain.mat
4 m" {$ t# X9 m0 b. t1 D7 S$ e" | │ │ submit.m
- i4 _6 @* _" i) D1 K" E! @; { │ │ submitWeb.m
* c: ]7 k* @1 c/ a2 x( A0 q │ │ svmPredict.m# o3 [7 e% }8 O( E# h& H
│ │ svmTrain.m# \2 s) k2 P+ @+ Z1 D. {, ?
│ │ visualizeBoundary.m0 f! r! M0 G/ d7 V% d5 U
│ │ visualizeBoundaryLinear.m4 n! c3 T& c* Z$ T9 f
│ │ vocab.txt
d. r3 f3 j- x2 N; Q" O& o0 m/ R4 j │ │
' r7 |% F* R: J- o6 h │ ├─ex71 ?6 g/ S- ^5 n$ ~8 `5 [: o
│ │ bird_small.mat8 l3 M* U5 M I# X8 }2 P6 ~
│ │ bird_small.png
2 ^/ o! J; A9 T* K' b; C9 }& r) w- B │ │ computeCentroids.m6 r( {" D, o, X! Q D
│ │ displayData.m
0 r4 S2 A9 Y) }. `/ @- N; L5 ] │ │ drawLine.m
1 G- Z2 C9 m8 N, d/ Q- g6 R4 V │ │ ex7.m
+ _+ v' k1 c' S, u0 |7 N │ │ ex7data1.mat
1 b1 q6 |/ L1 @5 ]) d4 p0 l+ h+ J. f │ │ ex7data2.mat
( S1 Q$ O9 R) F' S: ` │ │ ex7faces.mat* s* W; M6 A5 N3 A* z# L6 d4 g. o. n
│ │ ex7_pca.m9 N+ s. N! A3 N7 q/ R7 z4 u5 W
│ │ featureNormalize.m
# i1 d/ i- v. p& g# R& W# i3 ?6 J) S │ │ findClosestCentroids.m
5 r. [# \6 q. h' k$ M+ ` │ │ kMeansInitCentroids.m
' L) S# A6 B% p- Q0 b │ │ ml_login_data.mat9 G: r/ ] P( `! D o h, I1 y' B7 I
│ │ octave-core
7 t" B+ V3 T! x/ }4 O; d7 m │ │ pca.m8 [% ~% F6 r8 U3 Z7 s% A$ r
│ │ plotDataPoints.m
+ S9 H- p# I: f │ │ plotProgresskMeans.m
( l+ y; ^6 B$ r │ │ projectData.m* ]2 t. {7 M: W+ ]- S8 ^
│ │ recoverData.m# g i Q8 j: U
│ │ runkMeans.m. c7 j. w4 X- |4 Z) G; h: f" \
│ │ submit.m
! F0 L' L* ]4 V │ │ submitWeb.m
$ D' {' R8 l' `5 d3 A$ a8 C │ │ 7 Z$ {* G z) Y. I
│ └─ex8 z5 w% m! Y4 i
│ checkCostFunction.m
/ c& B# a6 r8 W, v │ cofiCostFunc.m
% i( Q5 z7 y# B# F5 l* d2 W │ computeNumericalGradient.m2 P) d9 e j' `8 k" I
│ estimateGaussian.m
! u7 a$ P) V# ^3 O+ G │ ex8.m" }2 X+ D: Z' _4 b5 k: J: t
│ ex8data1.mat
: L/ k5 S2 u( L/ E │ ex8data2.mat0 f# E- i0 n1 h& \$ y
│ ex8_cofi.m
+ B% q4 A9 M @' f; J │ ex8_movieParams.mat
; J6 E. k4 p3 N' |8 L │ ex8_movies.mat* w5 ?0 N/ w& }3 n
│ fmincg.m
3 G2 f. r' |$ ~ t" C │ loadMovieList.m
# _3 Q4 \( D J9 z" K6 m2 r │ ml_login_data.mat2 t. K* V2 U& `: X$ X. |$ K
│ movie_ids.txt) b& m# K. Z5 M. |, J
│ multivariateGaussian.m
/ T' H8 _. b1 @' F4 e: A6 ]6 s/ q │ normalizeRatings.m$ [9 j: [) O8 n1 f5 {; Z
│ selectThreshold.m
+ Z8 Y, y" i% b: O1 Q0 t) ~0 S4 m2 x │ submit.m
! x/ s5 M) v; \# ? │ submitWeb.m: f; i; d! f. ]: v) m
│ visualizeFit.m. a' Z. }# S- j- [
│
B0 Q' j' m. T- S; J ├─003_机器学习基石/ ^% L) ?0 [1 j+ t, T
│ └─003_机器学习基石+ Y6 w2 D5 [( C) I' x' k
│ │ 下载说明.txt
% G, p; L; I' r4 n0 l0 i5 V │ │ 关注我们.png
7 D4 D2 m: A) p B' n3 ] │ │ 攻城狮论坛=网络技术+编程视频.url
! B" y( } m6 b, B$ l/ a │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt: ~. J; B# x" v
│ │ " @ m- }. @0 N- c) M
│ ├─Machine Leaning Foundations - TNU
. e& x0 Q. D5 l& }7 i │ │ 01_handout.pdf0 V' J- y/ K/ y' L8 r; Y$ I; l
│ │ 02_handout.pdf
; q. ]3 {0 G6 c │ │ 03_handout.pdf8 P1 Z- Y& i; A9 g5 @) A0 t
│ │ 04_handout.pdf: ]- Q8 C) r6 _! \
│ │ 05_handout.pdf! i( p( p) {/ r
│ │ 06_handout.pdf
( A0 u+ U; h7 U, E$ ~% K │ │ 07_handout.pdf
& Q9 e& m" L/ Z( h* t6 M2 z k │ │ 08_handout.pdf
8 {& g- H0 p2 W$ { │ │ 09_handout.pdf+ u4 G* e! N6 F# o: y
│ │ 1 - 1 - Course Introduction (10-58).mp4
3 R5 }8 W; B3 E/ [+ `8 ^. T │ │ 1 - 2 - What is Machine Learning (18-28).mp4
: p+ E1 v! i5 o8 B, v7 \ │ │ 1 - 3 - Applications of Machine Learning (18-56).mp4. N2 f& \; z! h+ m
│ │ 1 - 4 - Components of Machine Learning (11-45).mp4
' K' e' z' g& ?* {9 [ │ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4
6 |. \1 H5 ^0 `3 S │ │ 10 - 1 - Logistic Regression Problem (14-33).mp46 U7 l9 ^% _. x# h* S- I( F
│ │ 10 - 2 - Logistic Regression Error (15-58).mp46 i* T" ~2 i/ j: ]9 ~
│ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4# T5 o4 N/ p$ e E. D
│ │ 10 - 4 - Gradient Descent (19-18).mp4' d" I" j% P+ n D" G l/ O, k
│ │ 10_handout.pdf% ~/ m. {6 c' G6 l6 y
│ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4
3 c) I7 Z# {( A6 N2 J2 c │ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4
9 b- k% c9 _9 r7 }- u. J1 D │ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp47 |6 l) i/ D1 A% `. C; D
│ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4
# D T: G- @% c2 f │ │ 11_handout.pdf7 {( j* b+ n/ K8 J4 k0 \
│ │ 12 - 1 - Quadratic Hypothesis (23-47).mp41 W, c, {6 L7 W; G7 ?
│ │ 12 - 2 - Nonlinear Transform (09-52).mp4, ~- G0 |4 b( ~( J! j3 C! c: E) [
│ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp4
7 \+ s+ b" ?) ~0 P │ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4& k* T% f3 m9 c' f2 [. f! P/ |
│ │ 12_handout.pdf/ c k% j! x' S' G4 ?$ g% C
│ │ 13 - 1 - What is Overfitting- (10-45).mp4- h: z5 [+ f& i* N
│ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4
o4 i7 m0 o9 G7 v" E! ?$ Q │ │ 13 - 3 - Deterministic Noise (14-07).mp4
; _6 w6 z ^4 R5 N3 Z │ │ 13 - 4 - Dealing with Overfitting (10-49).mp4% p7 C7 f% G: q
│ │ 13_handout.pdf% p9 Z! N; a/ Y F0 x; [
│ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4; ?- M S& d& ~
│ │ 14 - 2 - Weight Decay Regularization (24-08).mp4* x1 v4 u" q" z0 r# y! }
│ │ 14 - 3 - Regularization and VC Theory (08-15).mp4
6 } N& D3 p0 q, I* n │ │ 14 - 4 - General Regularizers (13-28).mp4! n, s0 g( I& A9 e }8 T
│ │ 14_handout.pdf* v d% y- M. d6 `. r3 r) _3 i8 E
│ │ 15 - 1 - Model Selection Problem (16-00).mp4
* G9 ~, c( x, i1 y9 d$ x │ │ 15 - 2 - Validation (13-24).mp4- C. M3 Q: ] w. e
│ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
+ d- V* \) x1 W2 ]5 X" u% Z# x1 d │ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4
4 V7 O, Q0 c2 r │ │ 15_handout.pdf
! \# F/ T; u# U- f+ N/ R7 Q. N$ Z │ │ 16 - 1 - Occam-'s Razor (10-08).mp4
8 [. o* [: x9 F+ i. @ │ │ 16 - 2 - Sampling Bias (11-50).mp44 T7 w4 O; P+ |
│ │ 16 - 3 - Data Snooping (12-28).mp4
- U1 O p4 C: F5 l, q │ │ 16 - 4 - Power of Three (08-49).mp4
4 A/ V% K2 W. O( g │ │ 16_handout.pdf
* [/ X2 v* E2 }2 M5 L │ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4% P# W4 ]. X2 _$ u" z
│ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp49 U$ `" [& V$ K0 ?+ A4 s
│ │ 2 - 3 - Guarantee of PLA (12-37).mp4
; \6 a6 }. L2 S' H' X │ │ 2 - 4 - Non-Separable Data (12-55).mp47 w9 U1 _4 b' m# f7 p
│ │ 3 - 1 - Learning with Different Output Space (17-26).mp4/ N4 r* I" N6 Z; {8 v8 W
│ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
6 a. w- T) Z* B% W2 C: ^. F │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4
* `# g. _8 F' ]3 Y0 m! d! s' N │ │ 3 - 4 - Learning with Different Input Space (14-13).mp4
9 H3 _) d5 H H; l │ │ 4 - 1 - Learning is Impossible- (13-32).mp44 t8 G F7 ?- C, |
│ │ 4 - 2 - Probability to the Rescue (11-33).mp46 ^. M3 v' u# e7 S! c
│ │ 4 - 3 - Connection to Learning (16-46).mp4/ c1 F& M& L: H7 V" k- p" T/ B
│ │ 4 - 4 - Connection to Real Learning (18-06).mp4
! k; G9 \8 ]+ I │ │ 5 - 1 - Recap and Preview (13-44).mp4
4 [( h# r0 O/ y9 {% k8 Q9 ? │ │ 5 - 2 - Effective Number of Lines (15-26).mp4
- |/ @4 P* |: o- U* ]6 l" u2 b │ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp4/ Q2 A$ B) ~; H. G& l% b# Y
│ │ 5 - 4 - Break Point (07-44).mp4
. {: [4 {. W$ O │ │ 6 - 1 - Restriction of Break Point (14-18).mp4
8 `) X( \: s4 c- u5 e │ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4- ~% M: R! s5 J& G1 M
│ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
; a) _$ N9 w# p6 d │ │ 6 - 4 - A Pictorial Proof (16-01).mp4
( i' g5 _# g) Z3 L: y │ │ 7 - 1 - Definition of VC Dimension (13-10).mp4
; q+ e6 a# o! n, z; R! x0 q │ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp4
1 W$ ^9 u- h h2 e8 O │ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4! n2 Q8 w3 w1 T2 W2 M
│ │ 7 - 4 - Interpreting VC Dimension (17-13).mp4) q3 {8 T! w% s6 r' T
│ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4
, ]8 m8 N9 \6 Q) h8 \3 X9 H& ] │ │ 8 - 2 - Error Measure (15-10).mp44 s+ e% R+ z) h# N/ A
│ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4 ]- @! r) G! {* O; i7 d! C
│ │ 8 - 4 - Weighted Classification (16-54).mp4
9 q+ q4 O# C: T │ │ 9 - 1 - Linear Regression Problem (10-08).mp4
7 m( ]! ~- w# V% {9 l% l │ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4
9 b3 J: ^/ z* Y4 K7 M │ │ 9 - 3 - Generalization Issue (20-34).mp4) Z+ y m% Z3 A8 y& i
│ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp4
! ~% W! z/ `/ t │ │ ' D" q9 W6 M/ m7 ^8 m/ x" t
│ ├─机器学习的基石
, ~1 ]: R: S9 g+ l' `& x1 _% G │ │ 1 - 1 - Course Introduction (10-58).mp4
9 g# B0 N( p4 X: P% G6 }/ N │ │ 1 - 2 - What is Machine Learning (18-28).mp4" |1 @) b, z, r! w, t. @
│ │ 1 - 3 - Applications of Machine Learning (18-56).mp47 C1 j0 f7 p' m8 h
│ │ 1 - 4 - Components of Machine Learning (11-45).mp48 A( {5 h+ B# k; i
│ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4 c% k- r9 ^' ]% m& ]
│ │ 10 - 1 - Logistic Regression Problem (14-33).mp4
& f( s" h. a5 X$ Q5 R │ │ 10 - 2 - Logistic Regression Error (15-58).mp4
: ^5 O9 @8 `# F │ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4
* i! Q4 t- ^, W, u+ E. m │ │ 10 - 4 - Gradient Descent (19-18).mp4
) v9 v& l' {0 q8 V, d3 J3 f' ^ │ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4. O* X* G% u |6 h7 x
│ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4
0 @5 b+ X6 s x4 r" o │ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp4! z9 A: d; I* }
│ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4 `/ v) T6 f% W7 z: A
│ │ 12 - 1 - Quadratic Hypothesis (23-47).mp40 l$ G$ ?' T2 P5 X
│ │ 12 - 2 - Nonlinear Transform (09-52).mp4' E2 ^4 ^3 L& n7 F# p2 p) F3 v
│ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp4
. D& s( V. E" ^ │ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4* p4 z5 Y1 ~' o& Q' v
│ │ 13 - 1 - What is Overfitting- (10-45).mp4
6 {$ p, g2 v: p! q! W$ y │ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4/ ]' f: c2 \( X0 N: P1 B
│ │ 13 - 3 - Deterministic Noise (14-07).mp41 i5 l) Z) j4 R& u* }. B) a
│ │ 13 - 4 - Dealing with Overfitting (10-49).mp4. J' o$ @5 L# S. c4 G5 ~
│ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4
" v) r" D* A# b; ~( T │ │ 14 - 2 - Weight Decay Regularization (24-08).mp4
P( }% `8 Q, }4 v/ J │ │ 14 - 3 - Regularization and VC Theory (08-15).mp41 Z: V# X9 F) K3 [/ }9 u
│ │ 14 - 4 - General Regularizers (13-28).mp47 W, f1 a, H% G" }- ]0 o
│ │ 15 - 1 - Model Selection Problem (16-00).mp4
8 g9 {& B4 j# k/ A+ s │ │ 15 - 2 - Validation (13-24).mp4. q0 f5 k. P* j& U
│ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4- I* z( @7 w* e5 S/ v
│ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4
7 O. P' s7 O& v! ~ u/ F: m; U2 m7 t& l │ │ 16 - 1 - Occam-'s Razor (10-08).mp4
+ t* B. y2 N7 A3 h6 @- D1 X │ │ 16 - 2 - Sampling Bias (11-50).mp46 W* v; s$ X; p8 R, U
│ │ 16 - 3 - Data Snooping (12-28).mp4& S$ t3 U C! f. m$ t1 M* ?( K% C) r
│ │ 16 - 4 - Power of Three (08-49).mp4
0 J9 @7 q1 Y7 { │ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4
! R2 l6 K( v% g1 [% V7 M& N5 f1 Y │ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4
$ Q6 P2 R3 [! t' R: F- J │ │ 2 - 3 - Guarantee of PLA (12-37).mp4
$ @5 c c8 Q3 Z. s/ W- \4 R │ │ 2 - 4 - Non-Separable Data (12-55).mp4
3 t$ k, J+ s3 A9 e/ [; x9 W │ │ 3 - 1 - Learning with Different Output Space (17-26).mp4
$ l) G! r* P8 P5 _7 v7 t* R) T │ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
0 C5 Y! m2 t( R0 t- H9 R- | │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4
7 ~8 W# l" q; [% P │ │ 3 - 4 - Learning with Different Input Space (14-13).mp4- L5 }7 f$ _0 p" o3 u: `! V6 s- r. E
│ │ 4 - 1 - Learning is Impossible- (13-32).mp4' |6 T* ]" ^7 O4 K: N; W
│ │ 4 - 2 - Probability to the Rescue (11-33).mp4
, ]- r* o U" A `1 m5 t& ?% a │ │ 4 - 3 - Connection to Learning (16-46).mp4
: R% t7 L1 y' H │ │ 4 - 4 - Connection to Real Learning (18-06).mp46 Y7 y. h& ^$ M4 q: |
│ │ 5 - 1 - Recap and Preview (13-44).mp48 ~- L) v) N- B) |* C
│ │ 5 - 2 - Effective Number of Lines (15-26).mp4' \+ J) p2 y4 U* L& Y
│ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp4- n7 s) S' F. ^! {
│ │ 5 - 4 - Break Point (07-44).mp4
" D" S, ?2 U5 U& h7 _ C, Y: d │ │ 6 - 1 - Restriction of Break Point (14-18).mp4; u* |2 @/ b( K4 h! q+ f% ?/ L! E
│ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4
! E* a4 y0 B" U0 B- } │ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4; m* r( g/ X2 A% N% f! S0 t6 E
│ │ 6 - 4 - A Pictorial Proof (16-01).mp4
5 C# }0 h) S$ B; u# S2 y │ │ 7 - 1 - Definition of VC Dimension (13-10).mp49 N8 w, W5 q. ^5 n3 `
│ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp4
# U) S- a! X- s( q% d! ^8 O │ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4! E' A! T F8 }( W
│ │ 7 - 4 - Interpreting VC Dimension (17-13).mp49 M' z* M" x- C' d) V
│ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4; }' ~7 N6 G8 c9 K& Y
│ │ 8 - 2 - Error Measure (15-10).mp4
: @% Z, R/ O3 S! W5 Q6 P │ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4) t$ m( l) `2 {. }0 v/ D( [% a
│ │ 8 - 4 - Weighted Classification (16-54).mp4" C2 B4 s4 d! F. i" E; }& @2 u
│ │ 9 - 1 - Linear Regression Problem (10-08).mp4
/ s' @1 _5 C2 @- m7 G& J │ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4, o$ D; J a+ I
│ │ 9 - 3 - Generalization Issue (20-34).mp4+ }" n7 ^0 _1 e7 E4 k
│ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp48 V* \# K- E Y% U& R3 h
│ │ lecture_slides-01_handout.pdf) Y$ q3 Y) ]/ R3 h3 v: `6 L; W
│ │ lecture_slides-02_handout.pdf
% l1 M* f$ l* y1 V+ D0 r; f │ │ lecture_slides-03_handout.pdf
: s9 g# h, f9 ^1 V0 E5 _ │ │ lecture_slides-04_handout.pdf
w* C7 _* O) n4 j$ Q, m │ │ lecture_slides-05_handout.pdf w* y) R. o: X$ A1 E# {
│ │ lecture_slides-06_handout.pdf
{5 g9 j0 R. D2 |$ h4 J │ │ lecture_slides-07_handout.pdf# V$ j/ M2 j' \6 |5 `. i
│ │ lecture_slides-08_handout.pdf1 E9 M5 g: P- b" ^- `
│ │ lecture_slides-09_handout.pdf" G1 L; m4 T$ A- S) X) u8 s$ j
│ │ lecture_slides-10_handout.pdf" u- O N& q" N1 P
│ │ lecture_slides-11_handout.pdf
! ?8 F6 f4 k+ w3 G │ │ lecture_slides-12_handout.pdf
. ]& r: }6 w8 s, Q │ │ lecture_slides-13_handout.pdf- p# _' k- i u: m. X
│ │ lecture_slides-14_handout.pdf
2 N: x+ e( b* I9 y4 l │ │ lecture_slides-15_handout.pdf
& N G# ]. f+ {0 R │ │ lecture_slides-16_handout.pdf
) @% X6 }4 M7 ^! J │ │
1 z$ k4 q- x* r4 _$ r% I) `7 _8 x │ └─機器學習基石% `/ }3 C7 W0 f: a9 K& I
│ ├─01_-_The_Learning_Problem
- X% L: n3 \* h* \% e( |- g9 o/ s3 T0 P& R │ │ 01_Course_Introduction_10-58.mp4
1 h2 r$ Y; Y; d6 V │ │ 01_Course_Introduction_10-58.pdf: G1 Q8 o0 ?5 m% Q$ B
│ │ 02_What_is_Machine_Learning_18-28.mp4# d# u1 H1 N8 W! ~ W% ^8 ^! j
│ │ 03_Applications_of_Machine_Learning_18-56.mp48 Z# I' _. h3 @7 K/ |# h* U6 ?
│ │ 04_Components_of_Machine_Learning_11-45.mp4
. c3 q$ a( |. X │ │ 05_Machine_Learning_and_Other_Fields_10-21.mp4% N3 I D$ Y0 I) J+ Q
│ │ 8 [# D) U9 _5 |$ r3 T
│ ├─02_-_Learning_to_Answer_Yes-No9 P7 u8 n5 C( \* c' G! P
│ │ 01_Perceptron_Hypothesis_Set_15-42.mp4' _ c V1 f- z# y& p
│ │ 01_Perceptron_Hypothesis_Set_15-42.pdf
6 W" ?9 g% e o │ │ 02_Perceptron_Learning_Algorithm_PLA_19-46.mp42 [& A8 t% w% { Y" M: M% p
│ │ 03_Guarantee_of_PLA_12-37.mp4
' P, t$ z) c; m1 L9 L7 e/ S5 u │ │ 04_Non-Separable_Data_12-55.mp42 g9 |: N9 D3 ?( m1 \5 y
│ │ 1 S2 ], d" w' f$ \, m6 X, n/ {
│ ├─03_-_Types_of_Learning
! ~( |& c- q8 o7 x8 ?& U │ │ 01_Learning_with_Different_Output_Space_17-26.mp4
& G. |, _2 R( J/ D │ │ 01_Learning_with_Different_Output_Space_17-26.pdf
7 u- P% T2 M: _/ |* V │ │ 02_Learning_with_Different_Data_Label_18-12.mp4
- ^, W3 h/ W: a6 R% { │ │ 03_Learning_with_Different_Protocol_11-09.mp4
- v; R6 ]$ Q4 |$ b. I/ } │ │ 04_Learning_with_Different_Input_Space_14-13.mp4
: Q3 h6 m8 [" c j4 _/ h │ │
9 S* X- u3 I( y( o6 ~) K │ ├─04_-_Feasibility_of_Learning7 Z, [+ v& m9 l2 S+ c) Z
│ │ 01_Learning_is_Impossible_13-32.mp4
$ j- E- i. i+ s# p* a0 ]4 r: ? │ │ 01_Learning_is_Impossible_13-32.pdf
3 v! {( a* W7 q* a, s' b │ │ 02_Probability_to_the_Rescue_11-33.mp4
1 g2 ]$ x' S! p% c │ │ 03_Connection_to_Learning_16-46.mp4
: g! O9 |# `. m# c( O. O g │ │ 04_Connection_to_Real_Learning_18-06.mp4) N9 D9 z6 ?) e4 F x7 W
│ │ & o; g8 B% [8 j9 T
│ ├─05_-_Training_versus_Testing
5 C' l- s- b8 d7 d( D- Q │ │ 01_Recap_and_Preview_13-44.mp4
4 G8 S9 h* `3 c+ c4 f │ │ 01_Recap_and_Preview_13-44.pdf- O/ _# d1 [" b) ]' j! p0 Z
│ │ 02_Effective_Number_of_Lines_15-26.mp4
" Z* B/ A. R% W, z2 Y3 \ │ │ 03_Effective_Number_of_Hypotheses_16-17.mp47 t) Q& H5 p0 X. f
│ │ 04_Break_Point_07-44.mp4( ^3 Z8 a- I9 `4 i4 ~4 [
│ │ & M8 _$ n. I- z* g5 b
│ ├─06_-_Theory_of_Generalization
% r* a1 S7 P! x# S% J │ │ 01_Restriction_of_Break_Point_14-18.mp4) Z2 u6 N Z6 d0 N
│ │ 01_Restriction_of_Break_Point_14-18.pdf
( |1 z1 t E6 j9 }/ `( ~ │ │ 02_Bounding_Function-_Basic_Cases_06-56.mp4 N% h, R, P) u+ l% l9 ^
│ │ 03_Bounding_Function-_Inductive_Cases_14-47.mp4" {! W7 L, p# E" K4 x6 ?3 ^
│ │ 04_A_Pictorial_Proof_16-01.mp4; e4 X8 n; x k8 ?7 [
│ │ 1 Z2 O6 E' ~8 a, Z" g/ Q$ r
│ ├─07_-_The_VC_Dimension
2 t- Y6 K, {% \) w │ │ 01_Definition_of_VC_Dimension_13-10.mp4
M/ n! T7 @! A# B │ │ 01_Definition_of_VC_Dimension_13-10.pdf
; u% d, T- V+ d; ^' _* a │ │ 02_VC_Dimension_of_Perceptrons_13-27.mp4
2 N# l$ ?- u+ I0 `1 R │ │ 03_Physical_Intuition_of_VC_Dimension_6-11.mp4
3 k, b+ U. \* d │ │ 04_Interpreting_VC_Dimension_17-13.mp4
6 V" M7 f1 @ Z3 Q │ │ ; f* f$ s8 n$ N5 T8 o, ]
│ ├─08_-_Noise_and_Error
7 n7 I6 H, B9 ], h; y2 M │ │ 01_Noise_and_Probabilistic_Target_17-01.mp4; U6 L" a3 A% D
│ │ 01_Noise_and_Probabilistic_Target_17-01.pdf0 ]( X L" O5 L) e
│ │ 02_Error_Measure_15-10.mp4% r% @3 S' w! {- `
│ │ 03_Algorithmic_Error_Measure_13-46.mp4, H' |' @4 j- Q: }1 j. }
│ │ 04_Weighted_Classification_16-54.mp4& Q4 a3 R6 F, ~9 k* E2 d& r
│ │
8 K k$ u8 S0 P5 k% P │ ├─09_-_Linear_Regression
$ D0 j, {1 U* p0 f/ f" o$ Y4 l │ │ 01_Linear_Regression_Problem_10-08.mp4
5 u+ I+ Q$ [" t: K, v* K& ] │ │ 01_Linear_Regression_Problem_10-08.pdf% P: a4 B5 B( Y2 b3 `+ \, Q
│ │ 02_Linear_Regression_Algorithm_20-03.mp4
, C5 W1 N4 N7 T& v │ │ 03_Generalization_Issue_20-34.mp4. Z. S8 z( `, L
│ │ 04_Linear_Regression_for_Binary_Classification_11-23.mp4/ [6 \' I& h" M5 ]2 C8 V8 |1 I
│ │ ) W# v- S' f( F
│ ├─10_-_Logistic_Regression
! |1 r3 M* k, A, J │ │ 01_Logistic_Regression_Problem_14-33.mp48 V3 Y8 e) M. p6 r
│ │ 01_Logistic_Regression_Problem_14-33.pdf! I' ?1 f! H+ l8 v, e+ J
│ │ 02_Logistic_Regression_Error_15-58.mp4$ O# A& q) t' x' b: N2 [ S
│ │ 03_Gradient_of_Logistic_Regression_Error_15-38.mp4+ n2 L8 _( n/ J6 i, A
│ │ 04_Gradient_Descent_19-18.mp4
7 x1 O" W, s1 a& D9 x) L0 a+ x │ │ * R" x7 @, [5 f/ V
│ ├─11_-_Linear_Models_for_Classification0 }1 Y. W6 ?8 ?: O5 G/ h
│ │ 01_Linear_Models_for_Binary_Classification_21-35.mp4 ]0 Q' Z) H! c# G
│ │ 01_Linear_Models_for_Binary_Classification_21-35.pdf
7 v9 `/ }" p; c │ │ 02_Stochastic_Gradient_Descent_11-39.mp4
0 c1 z6 ^8 z% V" G, i │ │ 03_Multiclass_via_Logistic_Regression_14-18.mp4, H1 s1 t! @. a' N6 n. R! H( d
│ │ 04_Multiclass_via_Binary_Classification_11-35.mp4
* m* `& ?) v3 k9 z9 `/ m4 E │ │ 0 C0 v% ]+ o+ g w! Q$ }% \; H! M0 h
│ ├─12_-_Nonlinear_Transformation
3 S9 n& Z3 a* q. i/ F │ │ 01_Quadratic_Hypothesis_23-47.mp4' j0 u K4 u. ]/ O* d
│ │ 01_Quadratic_Hypothesis_23-47.pdf$ v5 h3 E3 I( `! Q
│ │ 02_Nonlinear_Transform_09-52.mp4
8 Q! B% ]' Y9 K4 U9 D- k i │ │ 03_Price_of_Nonlinear_Transform_15-37.mp4
) Y. ~9 n$ }, [; j! K& i │ │ 04_Structured_Hypothesis_Sets_09-36.mp48 Y2 G! x/ Z2 G. i4 _5 Z
│ │
g' g8 z1 n/ w0 s │ ├─13_-_Hazard_of_Overfitting$ L! H: j; \6 }$ w
│ │ 01_What_is_Overfitting_10-45.mp4
: x" P" _/ x' k# |9 w1 [3 F$ ~ │ │ 01_What_is_Overfitting_10-45.pdf
8 {5 q+ a% E5 i$ Q7 {( V2 \! Y │ │ 02_The_Role_of_Noise_and_Data_Size_13-36.mp4
) S" Z1 U' X& X │ │ 03_Deterministic_Noise_14-07.mp4/ F9 t- Z6 m4 V2 d! n8 E, k# `9 W
│ │ 04_Dealing_with_Overfitting_10-49.mp4# n- K c9 N( e/ `& c' E4 j
│ │
9 z# j! {, P$ O B" p │ ├─14_-_Regularization) ]; @) U `$ _9 J- |1 L! G, _
│ │ 01_Regularized_Hypothesis_Set_19-16.mp4' D) I) a, l5 f: r7 \5 |/ W* S
│ │ 01_Regularized_Hypothesis_Set_19-16.pdf
% x% I4 E! {$ ?" q │ │ 02_Weight_Decay_Regularization_24-08.mp4" c ^" N: c( `7 p
│ │ 03_Regularization_and_VC_Theory_08-15.mp41 c/ ?* f) j1 N4 a ^+ W$ L8 x; V
│ │ 04_General_Regularizers_13-28.mp42 N( q( F0 g: I1 u: X9 ?( Y
│ │ ' E- w4 ` H7 e* V$ b& Q: z
│ ├─15_-_Validation! _7 ^- \, }9 L7 S& @6 n! R
│ │ 01_Model_Selection_Problem_16-00(437).mp4- f! F/ y i9 p
│ │ 01_Model_Selection_Problem_16-00.pdf
* V" \9 n$ L% K3 b. q* u0 a │ │ 02_Validation_13-24.mp4
& ~( e$ J# l+ w6 U │ │ 03_Leave-One-Out_Cross_Validation_16-06.mp4- ~6 v( [: E) R
│ │ 04_V-Fold_Cross_Validation_10-41.mp4/ J* I- S! c. U3 M0 w+ H% t
│ │
8 f% _$ y: E0 `) n6 j │ └─16_-_Three_Learning_Principles
% p% E4 i1 y/ o6 X6 f │ 01_Occams_Razor_10-08.mp4
9 U7 M+ f5 L) ?1 J/ p. C │ 01_Occams_Razor_10-08.pdf
, F) {! S% q, D! R │ 02_Sampling_Bias_11-50.mp4
" ?( f9 M3 z7 e6 ~& V7 x: b │ 03_Data_Snooping_12-28.mp4
. l0 \/ ~# k7 O B │ 04_Power_of_Three_08-49.mp4, I6 H- k* h7 {8 G- t: w+ F/ k
│
; |0 ?- I, d) n2 p2 h; Z* T. t5 ^/ L ├─004_机器学习技法/ K* n0 d0 k' q4 i, \
│ └─004_机器学习技法0 `, K4 a% W1 v* q0 G
│ └─機器學習技法1 `: L& A5 N2 x
│ │ 下载说明.txt
5 t: H$ p" |) ?! W. y │ │ 关注我们.png
- N3 ^8 K/ F8 S' u- f! s* k, p │ │ 攻城狮论坛=网络技术+编程视频.url1 T" P9 T4 `1 q
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt0 ~2 }0 Z4 @! ]0 R/ u2 J9 H
│ │ & D: {& o( M3 o; {5 m* _: _
│ ├─01_Linear_Support_Vector_Machine
8 p! P6 [' g' F- r; K% L2 y │ │ 01_Course_Introduction_4-07.mp4
3 T, |$ M. L% m$ X# d- u │ │ 01_Course_Introduction_4-07.pdf
0 L1 \+ f" v$ O: p │ │ 02_Large-Margin_Separating_Hyperplane_14-17.mp4
* ?0 l( O* V0 L4 ~, t │ │ 03_Standard_Large-Margin_Problem_19-16.mp45 Q" S$ R/ W0 H+ t- V" m
│ │ 04_Support_Vector_Machine_15-33.mp4, ^' }6 A4 H! b! O8 v+ h3 F
│ │ 05_Reasons_behind_Large-Margin_Hyperplane_13-31.mp4
# Z. ]( z0 J9 j3 u │ │
7 D8 y& W7 q$ @8 M* } │ ├─02_Dual_Support_Vector_Machine0 M, f c# k' n: t' C3 q1 [
│ │ 01_Motivation_of_Dual_SVM_15-54.mp4
5 S, E5 a' x( Q: D3 e │ │ 01_Motivation_of_Dual_SVM_15-54.pdf; b2 Z9 s' x3 f+ G* B0 s% \
│ │ 02_Lagrange_Dual_SVM_18-50.mp45 P! L$ J7 x9 j: o+ ?
│ │ 03_Solving_Dual_SVM_14-19.mp4* T- z' E5 u! X- x# A4 j; Y
│ │ 04_Messages_behind_Dual_SVM_11-18.mp49 g2 U. Q2 C6 s" C) v: ^8 b
│ │ 9 c' _8 Q0 w" ]; U; y- Z
│ ├─03_Kernel_Support_Vector_Machine
1 d: }& N7 }: j& P9 s, o │ │ 01_Kernel_Trick_20-23.mp4 \# K; c4 ~2 Z- }* U: n* ~
│ │ 01_Kernel_Trick_20-23.pdf b' }) O. u' b4 E) h' w8 [
│ │ 02_Polynomial_Kernel_12-16.mp44 V0 X1 Y2 M/ P3 W5 c% g3 [
│ │ 03_Gaussian_Kernel_14-43.mp43 w4 ?/ o& ]! J# s8 ]* ?# P" {
│ │ 04_Comparison_of_Kernels_13-35.mp4( L+ {$ ?# w9 i
│ │ ! j' I4 q3 {9 ]' L( d8 i" R' y2 h) Y
│ ├─04_Soft-Margin_Support_Vector_Machine
9 t" z, U8 l: u) |- F2 \+ ~9 L │ │ 01_Motivation_and_Primal_Problem_14-27.mp4
1 p6 A$ G! r; O0 B │ │ 01_Motivation_and_Primal_Problem_14-27.pdf" C* Z3 ~! r& b A" ^
│ │ 02_Dual_Problem_7-38.mp4
- P1 i- n9 C+ e │ │ 03_Messages_behind_Soft-Margin_SVM_13-44.mp4
- U$ G: R! q0 ?3 U │ │ 04_Model_Selection_9-57.mp4
# z9 o# d- z: V. [8 O3 H │ │
9 k0 w N: }# X │ ├─05_Kernel_Logistic_Regression8 W" f) {! I# P- T1 {9 T! r
│ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.mp4
- G7 i& ~( i* R$ @$ M3 h. S │ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.pdf
% ?6 j V" s+ Z( e │ │ 02_SVM_versus_Logistic_Regression_10-18.mp4
; @! a7 d; Y4 L. y3 t# r │ │ 03_SVM_for_Soft_Binary_Classification_9-36.mp4: ]' ] w5 V! I) s: m; |
│ │ 04_Kernel_Logistic_Regression_16-22.mp4
& v( o! {. R9 g& n# g │ │
5 e4 u8 R$ o& d6 f8 B │ ├─06_Support_Vector_Regression
3 B+ B' [+ ^3 f. g3 t* t │ │ 01_Kernel_Ridge_Regression_17-17.mp4. C" c2 O( |* d; R- x& s
│ │ 01_Kernel_Ridge_Regression_17-17.pdf
# v9 _& @" b* f; S │ │ 02_Support_Vector_Regression_Primal_18-44.mp4; p1 y' [7 m6 T4 O
│ │ 03_Support_Vector_Regression_Dual_13-05.mp47 A6 @( z9 y4 `3 ]. O
│ │ 04_Summary_of_Kernel_Models_09-06.mp4
# l; Y" q0 T: N4 W W+ @7 y. {: k │ │
/ S! F: I) R+ {# I! U' ]5 D# E2 ~ │ ├─07_Blending_and_Bagging/ V. Y3 |9 f6 u2 f# P
│ │ 01_Motivation_of_Aggregation_18-54.mp4
: h+ \( k7 ]5 O │ │ 01_Motivation_of_Aggregation_18-54.pdf
5 k* {3 N- C. A6 o │ │ 02_Uniform_Blending_20-31.mp4
2 n! x: H4 c$ D3 ]2 a │ │ 03_Linear_and_Any_Blending_16-48.mp4
: o% j z" p1 @1 s. F, x+ x) P │ │ 04_Bagging_Bootstrap_Aggregation_11-48.mp4
. A5 ?+ @; R8 l G' M/ | │ │
0 J+ ]. B2 E, U u, U, [: i8 u6 Y │ ├─08_Adaptive_Boosting% H l. R/ C* ?5 b$ V
│ │ 01_Motivation_of_Boosting_12-47.mp4
/ `* I& l( i# \) v │ │ 01_Motivation_of_Boosting_12-47.pdf
5 u/ ^; K, z- Y' F1 O │ │ 02_Diversity_by_Re-weighting_14-28.mp4
+ [4 [" {" U( L" ]% z3 W! J │ │ 03_Adaptive_Boosting_Algorithm_13-34.mp4: w5 \ x7 m4 d1 X- @9 M# _2 M
│ │ 04_Adaptive_Boosting_in_Action_11-04.mp4
+ n0 u& f7 H s5 }- i │ │ 0 h* l+ ~2 i: z& N3 T
│ ├─09_Decision_Tree
6 H! T! D4 G8 Z( ~" o8 k& Y7 t c; H │ │ 01_Decision_Tree_Hypothesis_17-28.mp4
3 Y; I/ _9 R! w# a) d# \3 ^" ] │ │ 01_Decision_Tree_Hypothesis_17-28.pdf
, m& j$ {/ K5 {$ j8 e; c6 B8 ` │ │ 02_Decision_Tree_Algorithm_15-20.mp4: _, c) @) C! \5 R, @0 \9 x: k
│ │ 03_Decision_Tree_Heuristics_in_CRT_13-21.mp4
1 q& d) W& z/ H │ │ 04_Decision_Tree_in_Action_8-44.mp4
% \, {5 `* ^8 v! z% D: W: f │ │ 0 P$ _" H8 J4 g9 J$ M& E+ s u
│ ├─10_Random_Forest
! U4 g9 }7 q! I │ │ 01_Random_Forest_Algorithm_13-06.mp4* h$ C1 M6 n$ A$ M
│ │ 01_Random_Forest_Algorithm_13-06.pdf3 l: z; ? F( ~' s+ R- b b
│ │ 02_Out-Of-Bag_Estimate_12-31.mp4
0 D) S+ X6 E( _( j) A- g │ │ 03_Feature_Selection_19-27.mp4, Z6 n6 d0 H: {+ q
│ │ 04_Random_Forest_in_Action13-28.mp4
8 F' V! j. `1 n( y$ \ c │ │ 2 W0 c" ]1 |$ \# [6 I! d o/ D
│ ├─11_Gradient_Boosted_Decision_Tree
( P6 F; a" t% x8 p: m │ │ 01_Adaptive_Boosted_Decision_Tree_15-05.mp4. }3 P5 M$ L" i: P! B' V( C9 [; @
│ │ 01_Adaptive_Boosted_Decision_Tree_15-05.pdf
) q r8 c3 X5 w. ? │ │ 02_Optimization_View_of_AdaBoost_27-25.mp4
/ r; \- M$ `5 c* @+ ^" o/ @" F$ m │ │ 03_Gradient_Boosting_18-20.mp4
' a: d$ O, ^3 C8 i# w │ │ 04_Summary_of_Aggregation_Models_11-19.mp4
# j6 _/ @+ m! d# F4 ]* F │ │ % S8 i% N8 R0 t2 A8 b
│ ├─12_Neural_Network2 i/ b" @/ X/ ^& V( y$ C( U
│ │ 01_Motivation_20-36.mp4 \7 G, |; @5 P& ]- y8 X2 k" K
│ │ 01_Motivation_20-36.pdf" p2 b! _& b$ u) H
│ │ 02_Neural_Network_Hypothesis_18-01.mp4
3 @+ m! [" Q. v9 O" S/ V │ │ 03_Neural_Network_Learning_22-26.mp4
6 _1 B; ~- o& Q │ │ 04_Optimization_and_Regularization_17-29.mp43 W2 ]* Y: Y9 e" g" k ?% A! V0 u! d
│ │ ' j1 P9 ?+ M3 R1 ?. A
│ ├─13_Deep_Learning. u1 A$ t/ g) e$ Q! x
│ │ 01_Deep_Neural_Network_21-30.mp40 X$ p2 Y; c2 s* u' }2 |
│ │ 01_Deep_Neural_Network_21-30.pdf) s0 _9 P; z3 x: M" k+ a7 z
│ │ 02_Autoencoder_15-17.mp4
2 E8 N1 ]4 e( U │ │ 03_Denoising_Autoencoder_8-30.mp4
' G; [2 Y9 ]! l: J. w1 V' y │ │ 04_Principal_Component_Analysis_31-20.mp4
Q# _" R) s" }, Y7 g" k& ~# l │ │
+ ]$ g! w- ], k2 y, y0 F │ ├─14_Radial_Basis_Function_Network
5 q: e; @1 }8 x$ u, n2 C% q │ │ 01_RBF_Network_Hypothesis_12-55.mp4
4 R. U) [7 F% u& M# [2 I, ?: G │ │ 01_RBF_Network_Hypothesis_12-55.pdf) I: Z6 E- B+ [/ M* k
│ │ 02_RBF_Network_Learning_20-08.mp4
, z, R" _& {) i& ]9 T* p! c7 a │ │ 03_k-Means_Algorithm_16-19.mp4
# l+ M9 @2 S0 q) f! ?7 [- X. N │ │ 04_k-Means_and_RBF_Network_in_Action_9-46.mp4
2 I7 y% e) s- F+ T& L │ │ 4 J& c/ f; k0 I, I/ N
│ ├─15_Matrix_Factorization
: g! r0 h! T) l2 N- u! X │ │ 01_Linear_Network_Hypothesis_20-16.mp40 _" g4 H; N: }! U5 e: Q, c
│ │ 01_Linear_Network_Hypothesis_20-16.pdf; e0 [( O2 x+ a8 T& H9 ]0 |% h
│ │ 02_Basic_Matrix_Factorization_16-32.mp4/ N: x$ O) z* u8 H2 C
│ │ 03_Stochastic_Gradient_Descent_12-22.mp4
+ E/ [ g' x4 Q, ]( \, @ │ │ 04_Summary_of_Extraction_Models_9-12.mp4
?+ y1 R$ R0 p$ O │ │ 4 t. a( u, s7 E, `& x
│ └─16_Finale
6 P8 z3 G/ k1 V1 H4 c" n │ 01_Feature_Exploitation_Techniques_16-11.mp4+ A7 [: \9 c0 A$ `0 p5 W: F# h
│ 01_Feature_Exploitation_Techniques_16-11.pdf
, h- r8 B* ? G" m+ Q3 u3 \! g │ 02_Error_Optimization_Techniques_8-40.mp4# |1 W; w* r5 @7 ~ ~
│ 03_Overfitting_Elimination_Techniques_6-44.mp4. e, R1 \) i/ ~5 b! V n
│ 04_Machine_Learning_in_Action_12-59.mp4
! x6 z1 w# W. J" Q │
* ], q% S+ I9 M* f ├─005_Neural Networks for Machine Learning
& Y. v5 ~3 v R- n; `2 Z │ └─005_Neural Networks for Machine Learning0 {( o8 a" M2 u; `6 e
│ │ 下载说明.txt* |4 W& B0 w7 H/ q+ @
│ │ 关注我们.png9 q8 V g$ l+ u* B
│ │ 攻城狮论坛=网络技术+编程视频.url
$ }& I2 H) H( w │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
1 b7 S4 ]4 X0 A' q8 a: Z& U │ │ 0 B8 l. c+ n( |7 U* H
│ ├─hinton-ml v% e4 H* l7 w, l1 P
│ │ 1.Why do we need machine learning( D4 Z3 |: x* h
│ │ 1.Why do we need machine learning.mp4
' i6 E. H/ y5 b │ │ 10.What perceptrons can't do [15 min].mp4
0 Y+ e: e$ c. X* @ │ │ 10.What perceptrons can't do [15 min].srt
+ z5 p; o6 l& h) w2 g$ W │ │ 11.Learning the weights of a linear neuron [12 min].mp4
' Y5 |$ h3 T7 K) C$ F │ │ 11.Learning the weights of a linear neuron [12 min].srt
+ k# I3 B* b8 N: M │ │ 12.The error surface for a linear neuron [5 min].mp4* m8 P6 `6 q! k& d( t/ V& c3 p
│ │ 12.The error surface for a linear neuron [5 min].srt& g# W8 t- n; |) f, o
│ │ 13.Learning the weights of a logistic output neuron [4 min].mp4- P9 S d+ K9 q; W5 d
│ │ 13.Learning the weights of a logistic output neuron [4 min].srt
; u: _3 n1 H* e- ~, Q* B; n2 C) P │ │ 14.The backpropagation algorithm [12 min].mp4& U, h& u( h; v
│ │ 14.The backpropagation algorithm [12 min].srt. R. N7 _; f o
│ │ 15.Using the derivatives computed by backpropagation [10 min].mp4$ d; [+ `- E4 c" o* r6 d
│ │ 15.Using the derivatives computed by backpropagation [10 min].srt
/ `3 y5 n% A4 `: e6 h3 F4 j3 o │ │ 16.Learning to predict the next word [13 min].mp4/ H/ [2 j8 G; Z9 w0 ~) s( N& c$ A
│ │ 16.Learning to predict the next word [13 min].srt
3 x# q* H3 B1 Z8 q- B │ │ 17.A brief diversion into cognitive science [4 min].mp4$ b* H+ b( N0 J) w5 N H
│ │ 17.A brief diversion into cognitive science [4 min].srt# Q/ m0 w& c. M6 G6 K' A
│ │ 19.Neuro-probabilistic language models [8 min].mp4
* W9 C: B& }& z% \! F' H( n8 O │ │ 19.Neuro-probabilistic language models [8 min].srt' v2 L* L1 ?: U/ J* F
│ │ 2.What are neural networks
- p+ G% y7 l1 q2 K: ~4 n1 _ │ │ 2.What are neural networks.mp4+ L) w% n: }/ ?* |2 v! o6 B1 F
│ │ 20.Ways to deal with the large number of possible outputs [15 min].mp41 d- Q& R; x- P
│ │ 20.Ways to deal with the large number of possible outputs [15 min].srt
- n P6 `2 f' W. d │ │ 21.Why object recognition is difficult [5 min].mp4
j% {5 s% z X8 J$ V. J │ │ 21.Why object recognition is difficult [5 min].srt
6 g: c6 s9 D$ d* p( ^5 l │ │ 22.Achieving viewpoint invariance [6 min].mp4- f& l! t+ x0 e5 h) }' L* U
│ │ 22.Achieving viewpoint invariance [6 min].srt7 ` r( d* m: s, V; M ~
│ │ 23.Convolutional nets for digit recognition [16 min].mp4
& \+ w0 z- c% [! I8 Z' j& C9 H: w │ │ 23.Convolutional nets for digit recognition [16 min].srt1 R) j# d# b6 \7 Q) U( P5 a
│ │ 24.Convolutional nets for object recognition [17min].mp4
& l# v% @: ~; }* B$ q │ │ 24.Convolutional nets for object recognition [17min].srt7 n$ ~; J0 n4 f: _0 ~- m$ @7 f/ f& v
│ │ 25.Overview of mini-batch gradient descent.mp4
$ g- v- ]( F4 } │ │ 25.Overview of mini-batch gradient descent.srt* _2 M. C% z. r6 T8 i. o3 I1 [
│ │ 26.A bag of tricks for mini-batch gradient descent.mp43 t$ `1 @$ L4 E" Z' `6 B8 k+ ~1 Q
│ │ 26.A bag of tricks for mini-batch gradient descent.srt. e0 C2 j0 d- c& p1 I
│ │ 27.The momentum method.mp4
: K) l8 \& V6 `( e* v& ~ │ │ 27.The momentum method.srt0 J( c. ?! P1 a# ^9 R/ {
│ │ 28.Adaptive learning rates for each connection.mp43 L0 j9 r# Z( ]% V
│ │ 28.Adaptive learning rates for each connection.srt; [# n. e; u( n/ K" J4 O8 J
│ │ 3.Some simple models of neurons [8 min].mp4$ Q1 S% p$ _5 b5 f% K
│ │ 3.Some simple models of neurons [8 min].srt
, Q. q; d3 ^* W5 A' s) g │ │ 31.Training RNNs with back propagation.mp4' v/ Z3 w7 _9 I4 G
│ │ 31.Training RNNs with back propagation.srt
/ p6 O8 [& n: y, x$ S; X$ h! `& { │ │ 32.A toy example of training an RNN.mp46 A* {. {, _4 b& k z3 ]7 x
│ │ 32.A toy example of training an RNN.srt
. ]1 O/ S- y$ n0 G9 k2 x, E, _0 p │ │ 33.Why it is difficult to train an RNN.mp41 C8 `. |8 m1 v5 O+ a i% g
│ │ 33.Why it is difficult to train an RNN.srt
$ l) e7 v% P3 |3 i │ │ 34.Long-term Short-term-memory.mp4% L+ X# U w+ C% m
│ │ 34.Long-term Short-term-memory.srt$ F6 h& ^2 D) g6 g4 x0 c( h' z; H- s
│ │ 35.A brief overview of Hessian Free optimization.mp4
# Q# ?# |7 r0 v3 j. Y1 T( _2 a D │ │ 35.A brief overview of Hessian Free optimization.srt7 `+ p! I3 V6 u; u7 g5 H& Q
│ │ 37.Learning to predict the next character using HF [12 mins].mp4
' y$ U2 l: y( k) z2 ^8 U6 y( f( a! s" I+ W │ │ 37.Learning to predict the next character using HF [12 mins].srt$ S- a% T2 i/ z4 p8 A" \
│ │ 38.Echo State Networks [9 min].mp4
/ H, y# m5 P0 m L3 w │ │ 38.Echo State Networks [9 min].srt" F: ]( O% H% l* d7 d. T. Z1 @
│ │ 39.Overview of ways to improve generalization [12 min].mp4
: \9 p8 v. X f/ B" Y( O │ │ 39.Overview of ways to improve generalization [12 min].srt
2 ]1 m0 ^; L+ p: I2 d3 T │ │ 4.A simple example of learning [6 min].mp4
& }5 B8 |0 c( ?' B4 f5 f │ │ 4.A simple example of learning [6 min].srt. N" y) C _. M
│ │ 40.Limiting the size of the weights [6 min].mp46 g" J# N0 J4 L' M7 X% w* b0 `) Y: n
│ │ 40.Limiting the size of the weights [6 min].srt
6 z6 m5 m B# Z │ │ 41.Using noise as a regularizer [7 min].mp4; N9 E T% @8 p6 h
│ │ 41.Using noise as a regularizer [7 min].srt
3 w9 S$ |9 |1 i" X$ M" D │ │ 42.Introduction to the full Bayesian approach [12 min].mp4
6 W& |* B7 q4 [, V │ │ 42.Introduction to the full Bayesian approach [12 min].srt/ X' ?9 P8 e5 l5 W& F" V! J
│ │ 43.The Bayesian interpretation of weight decay [11 min].mp49 ^! ~# ?/ a0 I! t0 p* n6 `& c9 C9 B0 ^
│ │ 43.The Bayesian interpretation of weight decay [11 min].srt
, q$ s+ f. `% v6 Y/ _: i5 {4 { │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].mp4
$ j+ I/ N/ n0 Q% F$ G& ] │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].srt
9 w$ X- O4 G6 D, A* h │ │ 45.Why it helps to combine models [13 min].mp4
& n; o3 i3 Z; h' f6 d │ │ 45.Why it helps to combine models [13 min].srt
5 Q7 m( M2 L) h2 t2 m3 x │ │ 46.Mixtures of Experts [13 min].mp4
; E1 O! P1 f% P9 F' j$ { │ │ 46.Mixtures of Experts [13 min].srt- H9 g9 d7 Z# H$ {" J2 r0 \! ?8 b, n
│ │ 47.The idea of full Bayesian learning [7 min].mp4
+ u4 E& J5 u6 _6 P │ │ 47.The idea of full Bayesian learning [7 min].srt
! r8 ^' g6 U* O, R+ }* k │ │ 48.Making full Bayesian learning practical [7 min].mp4/ w6 V& C' P- Q0 k0 [ S
│ │ 48.Making full Bayesian learning practical [7 min].srt: _, N/ X5 p. f8 g9 S$ ~
│ │ 49.Dropout [9 min].mp4
5 x7 P( d, N. w/ H2 P z& ^: w │ │ 49.Dropout [9 min].srt7 {3 s$ \% R2 n' [
│ │ 5.Three types of learning [8 min].mp4+ j$ a' U1 T" {7 V# ?+ F7 {5 z* g
│ │ 5.Three types of learning [8 min].srt+ M6 _! `7 m7 E( r
│ │ 50.Hopfield Nets [13 min].mp4' }9 y( A; S7 _5 }7 E& w& w; ]0 m" O! G
│ │ 50.Hopfield Nets [13 min].srt; j K; U8 I2 @2 C) c" i
│ │ 51.Dealing with spurious minima [11 min].mp47 I# f5 C7 b8 Y* m8 j- h0 `/ w% l
│ │ 51.Dealing with spurious minima [11 min].srt
0 d4 O9 S) f# b, l │ │ 52.Hopfield nets with hidden units [10 min].mp4$ T; j) n% d2 M, d4 W8 h/ l* e
│ │ 52.Hopfield nets with hidden units [10 min].srt
# i* N7 J: @! c% m9 I" w │ │ 53.Using stochastic units to improv search [11 min].mp4
; `# k$ j9 ] I& a% V: Q │ │ 53.Using stochastic units to improv search [11 min].srt; j$ I2 y. Z2 h+ y# H: O2 e1 n2 @$ k
│ │ 54.How a Boltzmann machine models data [12 min].mp4
: O$ Y+ H5 B( b2 i7 m │ │ 54.How a Boltzmann machine models data [12 min].srt3 R: I" }$ i1 r* U; {: w
│ │ 55.Boltzmann machine learning [12 min].mp4 B! i: o" {4 Y
│ │ 55.Boltzmann machine learning [12 min].srt
- J3 Q% Q& w4 e │ │ 57.Restricted Boltzmann Machines [11 min].mp4
) D" G7 t# m' X# m+ d& O# L │ │ 57.Restricted Boltzmann Machines [11 min].srt
7 `' r6 y9 v6 X( v" s5 w2 E │ │ 58.An example of RBM learning [7 mins].mp4 R: f2 A' B% B; \! x
│ │ 58.An example of RBM learning [7 mins].srt
$ h$ M$ W W, g6 C! C │ │ 59.RBMs for collaborative filtering [8 mins].mp4
. v7 u K# ~- G. s3 ]! i3 \ │ │ 59.RBMs for collaborative filtering [8 mins].srt+ ?# o) B- r; h- ~2 _' `/ K
│ │ 6.Types of neural network architectures [7 min].mp4- {( ]$ S: E! Q3 o% `9 O
│ │ 6.Types of neural network architectures [7 min].srt: a% N2 _+ Y; }4 w$ s/ u/ q; g
│ │ 60.The ups and downs of back propagation [10 min].mp4
; O; f9 i; [: b# U │ │ 60.The ups and downs of back propagation [10 min].srt
: A% B: R+ x) p$ p4 i" @- | │ │ 61.Belief Nets [13 min].mp4
! s; J7 A$ ?) a1 l0 D) `' c& S" [8 D* L │ │ 61.Belief Nets [13 min].srt
$ S! V# C9 d9 ?+ B6 e7 n8 t │ │ 62.Learning sigmoid belief nets [12 min].mp4
/ |# r+ J5 ~* m1 i- |6 @ │ │ 62.Learning sigmoid belief nets [12 min].srt
, q( G8 f1 N; |% e │ │ 63.The wake-sleep algorithm [13 min].mp4
0 r: i2 z( Z% B9 \8 y │ │ 63.The wake-sleep algorithm [13 min].srt- _) S9 k. v7 f: y- o" p+ T5 S
│ │ 64.Learning layers of features by stacking RBMs [17 min].mp47 t+ M: N5 F: O8 b0 m
│ │ 64.Learning layers of features by stacking RBMs [17 min].srt
+ `4 X# M- I; m" z6 L │ │ 65.Discriminative learning for DBNs [9 mins].mp4
, |- ^! T$ d6 G* O │ │ 65.Discriminative learning for DBNs [9 mins].srt/ O, o) o; s0 d/ x. n/ K2 Q- g* v
│ │ 66(1).What happens during discriminative fine-tuning/ ^8 J9 C, E# d/ \* {
│ │ 66.What happens during discriminative fine-tuning
) H) [- c5 a, P& k8 |+ U, C5 p │ │ 67.Modeling real-valued data with an RBM [10 mins].mp4( G9 J+ {' ^/ M: ~. g
│ │ 67.Modeling real-valued data with an RBM [10 mins].srt7 P, B% F! w. m
│ │ 69.From PCA to autoencoders [5 mins].mp4& a1 N( ^% P# k1 @/ f* i8 ]
│ │ 69.From PCA to autoencoders [5 mins].srt
a* l* _2 P3 W2 R6 x1 l │ │ 70.Deep auto encoders [4 mins].mp4
# r# ^, ]5 _# M4 H7 d/ u: |4 d7 v8 | │ │ 70.Deep auto encoders [4 mins].srt' ~/ |% f6 m6 |' O
│ │ 71.Deep auto encoders for document retrieval [8 mins].mp4' z- V3 S4 I- d6 j6 D
│ │ 71.Deep auto encoders for document retrieval [8 mins].srt7 p+ l3 g T K! P* R8 s8 |) \2 [% V8 I
│ │ 72.Semantic Hashing [9 mins].mp4
* R0 |* j/ j& {/ D$ @$ d: i │ │ 72.Semantic Hashing [9 mins].srt6 q, `+ e& \$ _9 b5 D
│ │ 73.Learning binary codes for image retrieval [9 mins].mp4
3 t% h1 j( S( U) g1 \4 ~ │ │ 73.Learning binary codes for image retrieval [9 mins].srt9 O; v+ |2 H; d) F5 @
│ │ 74.Shallow autoencoders for pre-training [7 mins].mp4
( @# \8 u# d& G' h& q: G │ │ 74.Shallow autoencoders for pre-training [7 mins].srt, _. W3 k3 u0 }9 T- ?- M' U9 ~
│ │ 8.A geometrical view of perceptrons [6 min].mp4; z2 D! Z+ y+ ~2 P* l# F2 @
│ │ 8.A geometrical view of perceptrons [6 min].srt. `4 \; S3 X4 e8 e
│ │ 9.Why the learning works [5 min].mp4
, s3 J' t& {7 l# u │ │ 9.Why the learning works [5 min].srt
2 `& K" r0 @9 L! S8 J/ c │ │
- o: b# Q7 O S8 R │ └─neuralnets-2012-001
. Q; R4 S& f" Y; J( @ │ ├─01_Lecture1* z5 \/ g4 s+ Y: ?. E$ e
│ │ 01_Why_do_we_need_machine_learning_13_min.mp4$ R* f9 V7 h7 f+ G2 I' H C7 R
│ │ 01_Why_do_we_need_machine_learning_13_min.pdf' G/ O* ` ?2 Z5 J
│ │ 01_Why_do_we_need_machine_learning_13_min.pptx
2 \# V( L# @% L │ │ 01_Why_do_we_need_machine_learning_13_min.srt
8 B I* N z4 S- | d, I │ │ 01_Why_do_we_need_machine_learning_13_min.txt
* S/ z; G, v6 p │ │ 02_What_are_neural_networks_8_min.mp4' Q& w8 D5 o( n$ M G) J
│ │ 02_What_are_neural_networks_8_min.srt
' l8 x/ N: o- V6 f \5 n │ │ 02_What_are_neural_networks_8_min.txt5 P% U: T% G- K. {. E" x$ C
│ │ 03_Some_simple_models_of_neurons_8_min.mp4) Q- l* }6 y6 d- l
│ │ 03_Some_simple_models_of_neurons_8_min.srt
6 q6 G1 J2 ]4 @6 d2 o' T │ │ 03_Some_simple_models_of_neurons_8_min.txt
1 m# S+ }- x1 L │ │ 04_A_simple_example_of_learning_6_min.mp4: y6 T! o a( S1 Y$ ^2 O" u
│ │ 04_A_simple_example_of_learning_6_min.srt( V. i* j, Q; |3 y% M
│ │ 04_A_simple_example_of_learning_6_min.txt% ?, f1 h/ [5 D, R6 ~
│ │ 05_Three_types_of_learning_8_min.mp4$ w" F* j6 H0 ^* D; u1 V, ]
│ │ 05_Three_types_of_learning_8_min.srt
5 Q* m+ J" h$ f- B1 u │ │ 05_Three_types_of_learning_8_min.txt! ]) n0 @2 a% l1 o5 W& F9 m- c
│ │
( q3 z0 d1 O2 G% d8 K │ ├─02_Lecture2
7 C4 w8 O: F& B' ?' I! J; ^ │ │ 03_A_geometrical_view_of_perceptrons_6_min.mp4
- K8 \1 x# c5 H$ \: s, }8 L4 r │ │ 03_A_geometrical_view_of_perceptrons_6_min.srt+ _* q) Q3 \+ e' u9 H, v0 _
│ │ 03_A_geometrical_view_of_perceptrons_6_min.txt2 q0 h) Z, s$ Y8 R" G
│ │ 04_Why_the_learning_works_5_min.mp4
$ y2 V$ B) C6 _/ F8 U! ] │ │ 04_Why_the_learning_works_5_min.srt
' y" @( g! x8 H. z5 e! I( V │ │ 04_Why_the_learning_works_5_min.txt* o$ ~4 a9 s& [- P
│ │ 05_What_perceptrons_cant_do_15_min.mp4& s2 z$ |& t3 x4 Z
│ │ 05_What_perceptrons_cant_do_15_min.srt
/ s1 p6 d# H# ]7 r3 v │ │ 05_What_perceptrons_cant_do_15_min.txt
4 a6 R! W, ~4 Z$ i6 s8 Q" H5 a │ │
5 t: |7 C1 p0 _4 x0 c0 @ │ ├─03_Lecture3
% ]4 L5 N; W- m │ │ 04_The_backpropagation_algorithm_12_min.mp4
0 r) M# T' D$ K0 y w4 a │ │ 04_The_backpropagation_algorithm_12_min.pdf7 F8 e, P) o6 g W( Y& j" N
│ │ 04_The_backpropagation_algorithm_12_min.srt7 k) P: c" R2 ~0 i& o8 F
│ │ 04_The_backpropagation_algorithm_12_min.txt$ q; C8 }! H0 _) X
│ │ ' R( \: C$ I# N" `& R/ X$ I) e P
│ ├─04_Lecture4
3 f/ G7 P3 N" m& i* D │ │ 01_Learning_to_predict_the_next_word_13_min.mp4
# T1 t% p' G; Y7 r2 B% b9 ~5 x+ @ │ │ 01_Learning_to_predict_the_next_word_13_min.pdf
U& C! K# ~! N' ~4 A1 ]$ ]3 ~ │ │ 01_Learning_to_predict_the_next_word_13_min.pptx+ c5 [' K3 x. C$ a# w* Y
│ │ 01_Learning_to_predict_the_next_word_13_min.srt4 o* Y% u" F. `" X
│ │ 01_Learning_to_predict_the_next_word_13_min.txt
5 u9 I8 A1 p# O: x! {) ` │ │ 04_Neuro-probabilistic_language_models_8_min.mp4
0 X/ A0 U _: k9 \1 U │ │ 04_Neuro-probabilistic_language_models_8_min.pdf
0 M9 c' s. b3 N0 l │ │ 04_Neuro-probabilistic_language_models_8_min.srt/ p& L7 w* a0 C' s* _
│ │ 04_Neuro-probabilistic_language_models_8_min.txt
# o, }! j- t+ J+ f │ │
. D- V/ ], c6 l' t4 l. w U8 q │ ├─05_Lecture5) M& d2 D: L) E, E
│ │ 01_Why_object_recognition_is_difficult_5_min.mp47 g+ d& f S1 q1 {
│ │ 01_Why_object_recognition_is_difficult_5_min.pdf/ {% H: D- h( l! Y
│ │ 01_Why_object_recognition_is_difficult_5_min.srt
" l. Z! G0 D) q │ │ 01_Why_object_recognition_is_difficult_5_min.txt% p- O% S0 ]' x2 g: G! b) ?2 \
│ │ 02_Achieving_viewpoint_invariance_6_min.mp4
* g5 S% \# ?* j% Q, c) f2 o │ │ 02_Achieving_viewpoint_invariance_6_min.srt0 _2 _6 o5 S6 P4 M4 m6 T( ?
│ │ 02_Achieving_viewpoint_invariance_6_min.txt
0 T0 M, c: W; C │ │
& @/ l: C& H0 N; u! r! V" V X' y │ ├─06_Lecture6- \* l, J) X7 K4 R2 g/ |4 z
│ │ 01_Overview_of_mini-batch_gradient_descent.mp4/ C1 y+ W, x- J+ N( K
│ │ 01_Overview_of_mini-batch_gradient_descent.pdf
3 E: P& g$ H7 C │ │ 01_Overview_of_mini-batch_gradient_descent.pptx& t4 @& l ^% g, B" u) z% @
│ │ 01_Overview_of_mini-batch_gradient_descent.srt3 ~+ ]/ G9 P) \
│ │ 01_Overview_of_mini-batch_gradient_descent.txt
, v4 b# M$ F4 ~; s* d" B/ ] │ │ 03_The_momentum_method.mp4% a- i4 q4 @. T- C' \
│ │ 03_The_momentum_method.srt$ }7 c e. C7 N6 Z- i* i
│ │ 03_The_momentum_method.txt
/ i: J- z0 R6 {( \5 J% G1 v$ Z& b9 K │ │
; C* |2 t% v, G/ w │ ├─07_Lecture75 Z% v7 {% p6 Z6 }
│ │ 01_Modeling_sequences-_A_brief_overview.mp43 c, v, W0 L, F9 L
│ │ 01_Modeling_sequences-_A_brief_overview.pdf/ `* c# Z$ N3 [6 {2 B4 d0 Z0 I
│ │ 01_Modeling_sequences-_A_brief_overview.pptx2 W3 {5 l% \. C2 b
│ │ 01_Modeling_sequences-_A_brief_overview.srt, U* p0 n }, J1 C, e3 V K
│ │ 01_Modeling_sequences-_A_brief_overview.txt
( f3 ^2 r i! ~( _ │ │ 02_Training_RNNs_with_back_propagation.mp4! R, `3 x0 B6 e+ \
│ │ 02_Training_RNNs_with_back_propagation.srt
6 ~7 i4 E4 F$ F! H1 ` │ │ 02_Training_RNNs_with_back_propagation.txt
6 u( i8 F! `4 v" {* T2 ]4 y) t │ │ 03_A_toy_example_of_training_an_RNN.mp4
7 ` j0 G# ^4 R+ a! K2 y- Q │ │ 03_A_toy_example_of_training_an_RNN.srt
- b$ s1 t. F6 b. d- A u7 `2 U( A% K │ │ 03_A_toy_example_of_training_an_RNN.txt7 Q& U$ P- p& |/ E6 F$ ~& K2 ]
│ │ 04_Why_it_is_difficult_to_train_an_RNN.mp49 j/ N1 r- D I0 i/ E* J* v X7 x# o. A
│ │ 04_Why_it_is_difficult_to_train_an_RNN.srt
" D: B. f; m( o* Y0 I- E │ │ 04_Why_it_is_difficult_to_train_an_RNN.txt
% F* j: p( a$ [) d/ K │ │ 05_Long-term_Short-term-memory.mp45 [5 l, n" A; Q4 Z" m9 j
│ │ 05_Long-term_Short-term-memory.pdf
6 ? [" e r, X" z% O2 l" |7 T │ │ 05_Long-term_Short-term-memory.srt
9 s! M8 s% ?1 A0 J" ^ │ │ 05_Long-term_Short-term-memory.txt" Y. z. m/ b$ r0 Q$ M
│ │ ( F& M( U" J% a0 ]7 N! v7 a
│ ├─08_Lecture8
" [4 j' t/ o+ r5 R │ │ 04_Echo_State_Networks_9_min.mp4; f/ K' r# R: `8 `8 q
│ │ 04_Echo_State_Networks_9_min.srt5 t- {/ L/ X" v/ p
│ │ 04_Echo_State_Networks_9_min.txt$ T5 G! M4 f5 B# z& F d* O- ^# k" L
│ │ 6 F0 k" ]+ w2 j) g$ m: z
│ ├─09_Lecture9* q4 x$ O0 \' i% M
│ │ 02_Limiting_the_size_of_the_weights_6_min.mp4
2 ?/ \$ `& K. D! D9 \7 e │ │ 02_Limiting_the_size_of_the_weights_6_min.srt
3 m! }3 t/ ], Q! f* d3 z$ C; O │ │ 02_Limiting_the_size_of_the_weights_6_min.txt* S/ T5 K3 Q& L, a! N4 k1 l
│ │ 03_Using_noise_as_a_regularizer_7_min.mp4
0 N* k/ v* F9 Z │ │ 03_Using_noise_as_a_regularizer_7_min.srt
8 T# h/ l Q# d7 U7 ^. C. F │ │ 03_Using_noise_as_a_regularizer_7_min.txt4 y% j. a: }2 ]* P \2 ^9 q
│ │ ; x+ g' R! h+ K6 G' }
│ ├─10_Lecture10
( h- j8 S! n. s) \; l1 ^ │ │ 01_Why_it_helps_to_combine_models_13_min.mp4
' b1 G5 Z2 P# g5 l; W │ │ 01_Why_it_helps_to_combine_models_13_min.pdf" X) J/ ~* u) H5 ?' Y! ?3 f
│ │ 01_Why_it_helps_to_combine_models_13_min.pptx; ?' |5 a4 T1 P# {2 \6 N5 r/ i, h
│ │ 01_Why_it_helps_to_combine_models_13_min.srt
) G( p6 f. C0 a) O │ │ 01_Why_it_helps_to_combine_models_13_min.txt
( i( R! S) J7 K9 R+ X │ │ 02_Mixtures_of_Experts_13_min.mp4
: |; N& K' P! s0 `, m5 c3 V! r$ u │ │ 02_Mixtures_of_Experts_13_min.pdf) p& J/ }8 E/ T( o
│ │ 02_Mixtures_of_Experts_13_min.srt
( D( ^! r5 E) D0 n. h4 o$ q& w │ │ 02_Mixtures_of_Experts_13_min.txt
0 N! {$ Q% Q% b( U3 o7 w! [& E8 ? │ │ 03_The_idea_of_full_Bayesian_learning_7_min.mp4
) w8 e6 X! T; L/ M: u6 s; X' L │ │ 03_The_idea_of_full_Bayesian_learning_7_min.srt2 R7 h5 P$ f# b6 ?
│ │ 03_The_idea_of_full_Bayesian_learning_7_min.txt: G; d* w& d0 U/ D/ X/ H2 K
│ │ 05_Dropout_9_min.mp4# f$ D, m- ]1 s' M4 t4 P9 j
│ │ 05_Dropout_9_min.pdf
; G$ s8 {9 m L: }1 {3 X: i5 O │ │ 05_Dropout_9_min.srt
: z& e) g2 p, b8 M │ │ 05_Dropout_9_min.txt
( C; m Y7 p" E" a0 y& Z J │ │
, Q" |" H0 E. K: o2 e( Y, K; ]3 G8 w │ ├─11_Lecture114 R, h; b# G2 ?
│ │ 01_Hopfield_Nets_13_min.mp4
+ p/ \0 @ p& P9 E/ w& Z/ C( H │ │ 01_Hopfield_Nets_13_min.pdf
$ x. r3 V! ~# n5 q │ │ 01_Hopfield_Nets_13_min.pptx3 O u$ J$ R+ R5 `% A8 D
│ │ 01_Hopfield_Nets_13_min.srt
& c+ y5 Z/ A) v. e │ │ 01_Hopfield_Nets_13_min.txt2 f1 t; Y( \- E; C$ X" {
│ │ 02_Dealing_with_spurious_minima_11_min.mp4
6 v) x: c1 l9 M" a! ^, N4 f: ` │ │ 02_Dealing_with_spurious_minima_11_min.srt
0 _- N& p' D/ ^' V: j6 V: R │ │ 02_Dealing_with_spurious_minima_11_min.txt4 `( I4 C9 `! |% d' @7 F
│ │ 03_Hopfield_nets_with_hidden_units_10_min.mp41 }' }; g1 n+ W) v8 R
│ │ 03_Hopfield_nets_with_hidden_units_10_min.srt
+ I: R H; p) s G │ │ 03_Hopfield_nets_with_hidden_units_10_min.txt
4 A2 a9 Y/ f- E _. o, d" |- G │ │
4 e1 X3 l' K! |6 e8 D! d. b │ ├─12_Lecture12
. W& A5 g7 |" d3 G$ t$ o7 g$ \ │ │ 01_Boltzmann_machine_learning_12_min.mp4
4 I( Q: s, _( h* e+ h$ l │ │ 01_Boltzmann_machine_learning_12_min.pdf
- B6 G! E/ {; [& Q; I8 ] │ │ 01_Boltzmann_machine_learning_12_min.pptx
% K* x8 O& U: I2 r) n& F: \ │ │ 01_Boltzmann_machine_learning_12_min.srt) C; e% ]/ O2 `, L/ G7 [- Q+ H
│ │ 01_Boltzmann_machine_learning_12_min.txt
; ?4 ?$ a" C! b/ ~ │ │ 03_Restricted_Boltzmann_Machines_11_min.mp4
7 J/ T2 y; U2 j$ G4 |" S │ │ 03_Restricted_Boltzmann_Machines_11_min.srt1 Y6 b2 e6 x$ _3 u
│ │ 03_Restricted_Boltzmann_Machines_11_min.txt( r8 T. z) w( {& `6 k% `
│ │ 04_An_example_of_RBM_learning_7_mins.mp4
- J8 d2 T/ _) J- v3 |; ? │ │ 04_An_example_of_RBM_learning_7_mins.srt
. d% o; Y7 _1 z5 M$ I9 l │ │ 04_An_example_of_RBM_learning_7_mins.txt
6 Q' l1 h' g/ \- p" t1 X) D! n │ │ 05_RBMs_for_collaborative_filtering_8_mins.mp4
' w) [( j9 ?! a7 Z │ │ 05_RBMs_for_collaborative_filtering_8_mins.srt
' ?. ]0 F9 T% d* e1 k, _ │ │ 05_RBMs_for_collaborative_filtering_8_mins.txt
( e# u. p* ?, h q │ │
$ Q8 z' c! S7 a6 G0 r5 _1 q* r/ [6 u │ ├─13_Lecture13
- b j; Y1 ]5 e! N' D. ?6 a: L │ │ 02_Belief_Nets_13_min.mp4
- I! u, n9 X" ^$ t+ k! ~ │ │ 02_Belief_Nets_13_min.srt
9 y6 S/ S7 i& M) ` B) R │ │ 02_Belief_Nets_13_min.txt
% B1 l: q4 S% J6 d# a$ R │ │ 03_Learning_sigmoid_belief_nets_12_min.mp4
) @: D1 _# y! j# T: J& q9 V; z │ │ 03_Learning_sigmoid_belief_nets_12_min.pdf
3 x0 ]4 Y4 E9 Z" | │ │ 03_Learning_sigmoid_belief_nets_12_min.srt
/ y8 e$ }; t+ k0 K' U │ │ 03_Learning_sigmoid_belief_nets_12_min.txt$ w" |% K: }$ i, ~
│ │ 04_The_wake-sleep_algorithm_13_min.mp4
( s V8 b9 f2 z" E │ │ 04_The_wake-sleep_algorithm_13_min.pdf
: s- P# N# N% _" X │ │ 04_The_wake-sleep_algorithm_13_min.srt- r7 Y1 X& [7 b/ u
│ │ 04_The_wake-sleep_algorithm_13_min.txt
5 s" c( W/ n q: [ │ │ 6 t0 p6 A) d3 S& v* d6 o% D9 k% {
│ ├─14_Lecture147 Y/ P; q; D! D7 t
│ │ 02_Discriminative_learning_for_DBNs_9_mins.mp4* n6 @$ x4 V$ @
│ │ 02_Discriminative_learning_for_DBNs_9_mins.srt8 z2 q4 |' \8 d( p6 i9 F: @6 s
│ │ 02_Discriminative_learning_for_DBNs_9_mins.txt" K+ l) U7 p. k" y2 u- n
│ │ 8 V6 E) C$ @: x5 R
│ ├─15_Lecture15
9 P# g+ E$ h6 p( _! g- p │ │ 01_From_PCA_to_autoencoders_5_mins.mp4( \7 b+ N) ? J. {" C3 H/ G' @
│ │ 01_From_PCA_to_autoencoders_5_mins.pdf
2 c2 j" N2 K( D& T │ │ 01_From_PCA_to_autoencoders_5_mins.pptx
7 H, u: ^5 ^) }+ W3 P │ │ 01_From_PCA_to_autoencoders_5_mins.srt
/ Z0 G7 ~1 Q5 v# y │ │ 01_From_PCA_to_autoencoders_5_mins.txt1 H* S) Q3 C( U. G$ k
│ │ 02_Deep_auto_encoders_4_mins.mp4
% y6 q2 s5 p. s6 l( Y3 B0 Q* ~ │ │ 02_Deep_auto_encoders_4_mins.srt0 u6 j7 f/ a, h. S1 u3 w" E; t
│ │ 02_Deep_auto_encoders_4_mins.txt* C1 E9 a" C3 _9 o" L% p
│ │ 04_Semantic_Hashing_9_mins.mp4
m8 _9 L, T: D% @ │ │ 04_Semantic_Hashing_9_mins.pdf
0 {) n' ^. O. R# h! t, o* U5 P │ │ 04_Semantic_Hashing_9_mins.srt
. H% ?. u+ h" b3 ]! F: m │ │ 04_Semantic_Hashing_9_mins.txt
! v9 x; _2 n. R% T( J! w │ │ 9 C# B6 p. A3 C0 Y. R( ~) C' e
│ └─16_Lecture16
# ~3 ^( Y3 K5 L" B │ 04_OPTIONAL-_The_fog_of_progress_3_min.mp4
5 _$ c6 w1 q- w9 V T/ N( x │ 04_OPTIONAL-_The_fog_of_progress_3_min.pdf/ h8 U' b$ L1 O9 S3 T) n
│ 04_OPTIONAL-_The_fog_of_progress_3_min.pptx
! j" n! P, ]% n b# O: n │ 04_OPTIONAL-_The_fog_of_progress_3_min.srt
7 M c8 l: j" i$ M/ o+ u; w j( b │ 04_OPTIONAL-_The_fog_of_progress_3_min.txt
2 b3 H: b% r* W! U, H$ _. q' p │ ; m1 z4 w9 j: Q- r6 g5 `; u2 t
├─006_Probabilistic Graphical Models! x, L+ [1 A$ O7 a' G5 E
│ └─006_Probabilistic Graphical Models. M" h+ M5 a/ [* e/ p5 j
│ ├─pgm-003+ R6 D) G f: R4 s
│ │ ├─01_Introduction_and_Overview
3 h7 y9 E' U1 C5 F6 \0 w │ │ │ 01_Welcome.mp4' {0 Y) q6 [/ |, N
│ │ │ 01_Welcome.srt; q9 G' m0 D7 H& B6 N) R$ u- ]
│ │ │ 01_Welcome.txt0 C# x# f" J, c
│ │ │ 02_Overview_and_Motivation.mp4+ o" u" P% i: z6 w7 L3 I9 \
│ │ │ 02_Overview_and_Motivation.srt
0 N7 Z2 D: }; }9 C │ │ │ 02_Overview_and_Motivation.txt
5 U2 D4 k% s; G% o' x │ │ │ 03_Distributions.mp4
5 }" h- n9 h2 q' }2 ]* g │ │ │ 03_Distributions.srt" G/ u z* f2 ~
│ │ │ 03_Distributions.txt
) b* p6 B4 H( `" Z8 F │ │ │ 04_Factors.mp4
: G0 P4 d. t) K$ X$ H) z7 R │ │ │ 04_Factors.srt7 h9 P& G0 U8 l1 b% C7 ?. W: x5 ?
│ │ │ 04_Factors.txt
: Z1 _) [8 E+ i6 F' S( d- P( j │ │ │
; s- s! b7 F) _' k& \8 b1 P {8 _ │ │ ├─02_Bayesian_Network_Fundamentals
& G/ F3 x; O/ ?; Q/ p- B- \6 L │ │ │ 01_Semantics_amp_Factorization.mp4
! {" ], J( g. K! J/ O/ x │ │ │ 01_Semantics_amp_Factorization.srt
6 P; q, e% V6 W8 N+ s$ C) q& |4 W │ │ │ 01_Semantics_amp_Factorization.txt: F/ M$ T$ ]# R$ Y [
│ │ │ 02_Reasoning_Patterns.mp4
s- d: N0 F$ X │ │ │ 02_Reasoning_Patterns.srt
% H% M% E# K) c- Z( V) u │ │ │ 02_Reasoning_Patterns.txt! g, W9 ]+ }6 X1 U
│ │ │ 03_Flow_of_Probabilistic_Influence.mp4- `$ z4 I2 a5 k* k
│ │ │ 03_Flow_of_Probabilistic_Influence.srt
6 M1 ~0 f4 `8 [. `# X3 G- \ │ │ │ 03_Flow_of_Probabilistic_Influence.txt8 @- W; `) D |- C8 F$ U
│ │ │ 04_Conditional_Independence.mp4
/ r! P/ x$ ]- d; p J+ s4 V │ │ │ 04_Conditional_Independence.srt
$ U4 e' E# X" v! D) G; @ │ │ │ 04_Conditional_Independence.txt
4 N& m" f. x8 D │ │ │ 05_Independencies_in_Bayesian_Networks.mp4$ x7 N4 w9 ^% ?* o3 p4 {& u4 t
│ │ │ 05_Independencies_in_Bayesian_Networks.srt& ^6 O6 J0 `8 V0 D: ~" e/ U
│ │ │ 05_Independencies_in_Bayesian_Networks.txt, W, j4 h" K* u( m8 ^# O; S
│ │ │ 06_Naive_Bayes.mp48 r' l$ J# W) P) m& k3 h
│ │ │ 06_Naive_Bayes.srt
4 s+ J) t$ v: y( O1 W │ │ │ 06_Naive_Bayes.txt3 R) J, J5 w* V: Z& i& L
│ │ │ 07_Application_-_Medical_Diagnosis.mp4, R3 ~, ?, R+ V5 N/ z
│ │ │ 07_Application_-_Medical_Diagnosis.srt: o6 ^8 d* |. Q% m3 N
│ │ │ 07_Application_-_Medical_Diagnosis.txt
! p6 O& d# O2 Z, I$ {" R │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.mp4
/ T$ a* y6 z9 ?. O; y* B │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.srt
; g% b6 z% e# ?5 f* o │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.txt
" Y' k+ |, j% b, m │ │ │
' d+ a8 g/ j- y) A │ │ ├─03_Template_Models
+ w# w4 Z( N1 x( o │ │ │ 01_Overview_of_Template_Models.mp4. N# {3 L, [3 l, t) X( m
│ │ │ 01_Overview_of_Template_Models.srt4 I/ q& c! C: s2 I+ `
│ │ │ 01_Overview_of_Template_Models.txt
" ]- T9 a. r; J6 Q' h │ │ │ 02_Temporal_Models_-_DBNs.mp4* ?( H, Q" N: C5 \& I
│ │ │ 02_Temporal_Models_-_DBNs.srt$ j$ O2 v7 I x& \8 R. T% @1 U
│ │ │ 02_Temporal_Models_-_DBNs.txt3 }. l5 c5 Z' h) p9 n
│ │ │ 03_Temporal_Models_-_HMMs.mp4 F" i2 c o. T# t. i i! D
│ │ │ 03_Temporal_Models_-_HMMs.srt1 V. B. {- Y) I* b1 J* V
│ │ │ 03_Temporal_Models_-_HMMs.txt0 Y' B: u! b# z& v
│ │ │ 04_Plate_Models.mp4
; X4 E7 }! E, g* k; j │ │ │ 04_Plate_Models.srt
: G1 }7 H2 o/ `! c' g/ c' U; H │ │ │ 04_Plate_Models.txt1 B9 ?! Q4 P7 {! s) ^* R- n
│ │ │ 4 x0 _- C p3 M, e
│ │ ├─04_ML-class_Octave_Tutorial3 Q" I; S$ J& }" `$ q1 |* L
│ │ │ 01_Basic_Operations.mp43 Y! W; ^1 |9 U! V- s
│ │ │ 01_Basic_Operations.srt
H) `, A2 ]6 `- f, b │ │ │ 01_Basic_Operations.txt; w8 ?! o1 E* C( G& }
│ │ │ 02_Moving_Data_Around.mp4
8 k' c7 I: Q; a7 V L4 b8 L │ │ │ 02_Moving_Data_Around.srt
9 H7 L) n6 z1 t6 m │ │ │ 02_Moving_Data_Around.txt
% [/ X' U" u' z6 P% w │ │ │ 03_Computing_On_Data.mp4
1 p1 ?# K6 W8 _$ f' g$ L" q+ y9 G7 j │ │ │ 03_Computing_On_Data.srt
0 ~ z) m; Q# u# a2 C+ L! a5 S │ │ │ 03_Computing_On_Data.txt ~2 ?; d* N5 P4 m0 U: W6 j: |
│ │ │ 04_Plotting_Data.mp4
$ c, y! I' A7 U% q$ B │ │ │ 04_Plotting_Data.srt5 T' x1 o# X5 Z1 g0 J
│ │ │ 04_Plotting_Data.txt4 S6 Q6 c. q! }; s
│ │ │ 05_Control_Statements-_for_while_if_statements.mp45 C4 y/ L, t4 ~4 y0 u( Q5 G
│ │ │ 05_Control_Statements-_for_while_if_statements.srt
& ^: r6 `0 C% L0 P! w5 z( ^. x- g( p, A │ │ │ 05_Control_Statements-_for_while_if_statements.txt
9 H5 L! c+ x }1 b │ │ │ 06_Vectorization.mp4# ]8 Y b3 ?; t: `+ m a
│ │ │ 06_Vectorization.srt( m/ E- _0 v$ E1 j" d
│ │ │ 06_Vectorization.txt& q( H7 V h& R) m2 G( [2 B. ~2 U
│ │ │ 07_Working_on_and_Submitting_Programming_Exercises.mp4
; r$ n3 [1 m/ R │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.srt
& Z! q/ {; D3 h3 o* ]$ D9 N! | │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.txt, E! Q8 I, l' f' \6 ]
│ │ │
# V: T' d( c+ f │ │ ├─05_Structured_CPDs; C- N- X1 ^7 s) [, w& s
│ │ │ 01_Overview-_Structured_CPDs.mp4
0 m4 [" B# w% I& N* i │ │ │ 01_Overview-_Structured_CPDs.srt1 x: Q/ `' P/ g9 @% M& C, t
│ │ │ 01_Overview-_Structured_CPDs.txt
1 i$ Y+ w( ~8 G! [+ j │ │ │ 02_Tree-Structured_CPDs.mp4
; l1 h s& |' v │ │ │ 02_Tree-Structured_CPDs.srt: G! U; ~1 F' X* R5 x! X
│ │ │ 02_Tree-Structured_CPDs.txt4 F/ n6 P% N0 P: z* b
│ │ │ 03_Independence_of_Causal_Influence.mp4- n, P2 [0 o4 x1 |1 d) p
│ │ │ 03_Independence_of_Causal_Influence.srt. c/ M) ^* G! g9 y" o, d
│ │ │ 03_Independence_of_Causal_Influence.txt
1 q8 j& I3 ?; ^. Z$ I9 f" k- q7 q% o │ │ │ 04_Continuous_Variables.mp4
" p0 [1 i, }7 L k: B+ `+ p │ │ │ 04_Continuous_Variables.srt6 ]) d) a+ a2 h8 N; G3 `) j( d6 G
│ │ │ 04_Continuous_Variables.txt
* N. o& s$ D9 r- y% P1 i+ L │ │ │
2 E. U% j1 C, o! M │ │ ├─06_Markov_Network_Fundamentals
% `7 Z" [7 p% I& P+ S │ │ │ 01_Pairwise_Markov_Networks.mp41 J9 L8 v; w/ a" [% b
│ │ │ 01_Pairwise_Markov_Networks.srt9 F; W* b, `* n9 X" K9 H
│ │ │ 01_Pairwise_Markov_Networks.txt
8 u: O1 X2 z8 ~ │ │ │ 02_General_Gibbs_Distribution.mp4
! K$ @. e: q1 L) |4 A8 E/ f1 Q │ │ │ 02_General_Gibbs_Distribution.srt0 o: ?$ W) x4 G* f
│ │ │ 02_General_Gibbs_Distribution.txt
% x/ S1 K; D8 U e p+ ^2 p- }( f& O- g │ │ │ 03_Conditional_Random_Fields.mp41 S( m& v: _5 n' x
│ │ │ 03_Conditional_Random_Fields.srt' G* k; a0 N7 k5 b" b! Y3 _
│ │ │ 03_Conditional_Random_Fields.txt
; H2 O2 k$ k3 ^% b& X/ q │ │ │ 04_Independencies_in_Markov_Networks.mp4
% I# R( K( n0 W& T │ │ │ 04_Independencies_in_Markov_Networks.srt
* w0 ^4 Q G2 e │ │ │ 04_Independencies_in_Markov_Networks.txt5 q: v- |' A8 [3 @3 j0 T
│ │ │ 05_I-maps_and_perfect_maps.mp4
+ K4 B4 p) o% ]7 W1 p$ F │ │ │ 05_I-maps_and_perfect_maps.srt+ r6 @: F1 \) l u; g
│ │ │ 05_I-maps_and_perfect_maps.txt
$ E+ {# F: y* R; x3 ^ │ │ │ 06_Log-Linear_Models.mp4
m, I9 ^0 B* X- }1 c/ a │ │ │ 06_Log-Linear_Models.srt- E3 ?' @' u/ X2 H! E e
│ │ │ 06_Log-Linear_Models.txt
5 Y3 w4 s5 Q5 w( l │ │ │ 07_Shared_Features_in_Log-Linear_Models.mp4
/ s! X, v% [. z0 p% S- ` │ │ │ 07_Shared_Features_in_Log-Linear_Models.srt
2 ?2 j8 J8 O2 C, w0 E6 t5 N │ │ │ 07_Shared_Features_in_Log-Linear_Models.txt
: _6 D, X* z a! _/ y0 _7 l, B% L │ │ │
1 z( m" D9 b% G/ o! v( Y │ │ ├─07_Representation_Wrapup-_Knowledge_Engineering
; x1 k! \, t6 H- l │ │ │ 01_Knowledge_Engineering.mp4' g* Z2 b. ]1 i5 W2 Y; D/ D0 Q
│ │ │ 01_Knowledge_Engineering.srt' e. { W# @& y( k$ y+ p7 K' N
│ │ │ 01_Knowledge_Engineering.txt, [. O+ N- l1 v1 l8 ^
│ │ │
& \8 i% k8 p- [/ T6 o1 H │ │ ├─08_Inference-_Variable_Elimination
) `2 h5 F+ Q# Y. U) c │ │ │ 01_Overview-_Conditional_Probability_Queries.mp4
. t, B: a- g9 C8 F │ │ │ 01_Overview-_Conditional_Probability_Queries.srt
- v7 q8 m4 ^. O# [$ g0 I │ │ │ 01_Overview-_Conditional_Probability_Queries.txt
3 |. Y$ U6 L" G* f* T: X9 T │ │ │ 02_Overview-_MAP_Inference.mp4
: k1 @/ B5 _' |( i │ │ │ 02_Overview-_MAP_Inference.srt& A. m6 N7 C2 M3 Q2 K9 ^
│ │ │ 02_Overview-_MAP_Inference.txt) [3 a+ U1 |# B- `; W
│ │ │ 03_Variable_Elimination_Algorithm.mp4/ ?, e5 u: s3 H, V2 y5 ]
│ │ │ 03_Variable_Elimination_Algorithm.srt! M' V3 a1 |4 {$ r8 h
│ │ │ 03_Variable_Elimination_Algorithm.txt
$ e7 g, t: m7 @! q │ │ │ 04_Complexity_of_Variable_Elimination.mp4
( d1 C$ `2 y% b* C f1 K │ │ │ 04_Complexity_of_Variable_Elimination.srt! X3 E2 [& I0 r
│ │ │ 04_Complexity_of_Variable_Elimination.txt3 `; k' F7 S3 o* l' B) h
│ │ │ 06_Finding_Elimination_Orderings.mp4
. N) O ^+ A" t Z9 i& x │ │ │ 06_Finding_Elimination_Orderings.srt
+ U0 t5 m" C% u6 [ │ │ │ 06_Finding_Elimination_Orderings.txt7 }+ ]+ Z% z2 j+ P9 [
│ │ │
3 E/ n: q; U- I │ │ ├─09_Inference-_Belief_Propagation_Part_1
3 ~! _% O. ]/ K3 g4 U6 Q7 N │ │ │ 01_Belief_Propagation.mp4
$ A! i# |! ~! n+ j# h% |8 b │ │ │ 01_Belief_Propagation.srt. M* g+ c0 S: q
│ │ │ 01_Belief_Propagation.txt, t) G" ^' D! ^+ |; ^( S8 l' E
│ │ │ 02_Properties_of_Cluster_Graphs.mp4
" v9 H; V @( v; X8 P │ │ │ 02_Properties_of_Cluster_Graphs.srt8 b' B0 x) |; i- ?/ n
│ │ │ 02_Properties_of_Cluster_Graphs.txt) R( ^3 k) r. q0 {6 r# f
│ │ │
( F0 i0 M$ E/ o8 n0 S# }) {7 n6 p' V │ │ ├─10_Inference-_Belief_Propagation_Part_21 b# Z, r# }- n) h- u6 Q+ _1 t+ b1 ~5 m
│ │ │ 01_Properties_of_Belief_Propagation.mp47 [, Z; n2 u O3 K
│ │ │ 01_Properties_of_Belief_Propagation.srt
" _4 T4 m* _; G1 t2 S │ │ │ 01_Properties_of_Belief_Propagation.txt; N$ E3 E7 J0 [+ C, E
│ │ │ 02_Clique_Tree_Algorithm_-_Correctness.mp4
1 W6 U: ^* z# Y3 E0 } p │ │ │ 02_Clique_Tree_Algorithm_-_Correctness.srt9 N6 L5 l& a1 B0 h" t; z
│ │ │ 02_Clique_Tree_Algorithm_-_Correctness.txt
1 S! D+ O4 H/ [2 _1 W │ │ │ 03_Clique_Tree_Algorithm_-_Computation.mp4
5 i% F; I2 S) R4 |- ]. w+ c │ │ │ 03_Clique_Tree_Algorithm_-_Computation.srt
% L( |5 v/ O0 s2 d% S │ │ │ 03_Clique_Tree_Algorithm_-_Computation.txt
. G9 G" m& h& U │ │ │ 04_Clique_Trees_and_Independence.mp43 i, W5 x8 R3 i# |5 e/ u3 D
│ │ │ 04_Clique_Trees_and_Independence.srt8 ~# w) O% _( B( I3 w6 v
│ │ │ 04_Clique_Trees_and_Independence.txt
* }% `) o% q- B$ r/ [ │ │ │ 05_Clique_Trees_and_VE.mp4
: C5 k/ ^4 H, c* z% x! J4 P, f5 V │ │ │ 05_Clique_Trees_and_VE.srt0 l* i3 J; W1 R+ K8 P# C
│ │ │ 05_Clique_Trees_and_VE.txt8 r( I& H2 h8 ?7 N' O
│ │ │ 06_BP_In_Practice.mp42 g8 [# E, o3 E, Z2 N# \
│ │ │ 06_BP_In_Practice.srt9 U/ k( y+ [3 `" {# ^! ]
│ │ │ 06_BP_In_Practice.txt$ Q/ R1 S) D! l
│ │ │ 07_Loopy_BP_and_Message_Decoding.mp40 i* H+ t* s, ]+ ^) v( y9 u
│ │ │ 07_Loopy_BP_and_Message_Decoding.srt
# L. I0 l( Y$ g& `) j* u$ c │ │ │ 07_Loopy_BP_and_Message_Decoding.txt: y, Y1 r f3 |; t5 `% M
│ │ │ 7 z9 k5 X! L1 s1 Z
│ │ ├─11_Inference-_MAP_Estimation_Part_1
/ H: t5 D: l2 y, I& z8 l │ │ │ 01_Max_Sum_Message_Passing.mp43 C' W" I0 E! h
│ │ │ 01_Max_Sum_Message_Passing.srt% d: F+ z# ^& X! j& V3 G3 ?
│ │ │ 01_Max_Sum_Message_Passing.txt [2 r ~ c: j7 W5 o, {! Y
│ │ │ 02_Finding_a_MAP_Assignment.mp4
/ x& `; X& L! @# W$ k │ │ │ 02_Finding_a_MAP_Assignment.srt
) G$ y) \1 ]9 _ │ │ │ 02_Finding_a_MAP_Assignment.txt" _7 Q3 x& E6 [: A
│ │ │ + n2 X% w& F" j+ P( b# h
│ │ ├─12_Inference-_MAP_Estimation_Part_2" D# i6 B M7 P$ T+ J+ x5 L, P
│ │ │ 01_Tractable_MAP_Problems.mp46 K8 ]" {, G2 b" y2 K
│ │ │ 01_Tractable_MAP_Problems.srt! b( O9 ?7 f! {7 F5 O
│ │ │ 01_Tractable_MAP_Problems.txt. [( v; x" t2 g8 ]
│ │ │ 02_Dual_Decomposition_-_Intuition.mp4
" f8 g; ], X. V& s7 b# r │ │ │ 02_Dual_Decomposition_-_Intuition.srt7 d& b& n& E, B3 ^
│ │ │ 02_Dual_Decomposition_-_Intuition.txt! o R( f" K5 v" y' B/ @
│ │ │ 03_Dual_Decomposition_-_Algorithm.mp4, d+ L" N A0 w% b- w1 d" f
│ │ │ 03_Dual_Decomposition_-_Algorithm.srt# g' ?! I! n- @' D$ U/ M; V& ^* @
│ │ │ 03_Dual_Decomposition_-_Algorithm.txt
9 ^8 m+ H3 O% {! O! J; c │ │ │
7 ~' q/ F( p' M │ │ ├─13_Inference-_Sampling_Methods& E) X! b' C9 p# `
│ │ │ 01_Simple_Sampling.mp40 H8 z% C3 A: G3 Y( B- f/ w6 k" ^# t
│ │ │ 01_Simple_Sampling.srt
' k. y: v: S: e" ] │ │ │ 01_Simple_Sampling.txt
" [8 [# X: X) O' F9 S2 a │ │ │ 02_Markov_Chain_Monte_Carlo.mp4
, j2 W1 R3 w+ w3 {2 x- P │ │ │ 02_Markov_Chain_Monte_Carlo.srt0 y- O. v% I& D6 x7 {; P0 i5 g
│ │ │ 02_Markov_Chain_Monte_Carlo.txt
' ~: m5 ^; a9 Q' [0 ^ │ │ │ 03_Using_a_Markov_Chain.mp4% p' a+ V7 j. a
│ │ │ 03_Using_a_Markov_Chain.srt! }0 Y; l8 w. h5 }# L: B; J
│ │ │ 03_Using_a_Markov_Chain.txt
7 M% X1 n7 g$ D │ │ │ 04_Gibbs_Sampling.mp4# Z! h# w S. u$ y
│ │ │ 04_Gibbs_Sampling.srt
3 K) P( c: X9 S: b5 } │ │ │ 04_Gibbs_Sampling.txt
8 [0 n! C0 D/ \* T d │ │ │ 05_Metropolis_Hastings_Algorithm.mp4
1 P+ y8 B# m: C7 T1 t │ │ │ 05_Metropolis_Hastings_Algorithm.srt- @7 b( @3 S: q1 i5 D( ~
│ │ │ 05_Metropolis_Hastings_Algorithm.txt9 ]/ ?( ]" {8 ]
│ │ │
1 w1 E/ y. Q; O │ │ ├─14_Inference-_Temporal_Models_and_Wrap-up
( m) F: |! T; y, ~) S7 ~$ R3 J │ │ │ 01_Inference_in_Temporal_Models.mp4 M4 h1 B+ t" D f
│ │ │ 01_Inference_in_Temporal_Models.srt
# K2 }% r+ @( ?* j$ I9 V │ │ │ 01_Inference_in_Temporal_Models.txt
( R4 d5 [% h+ h/ E u. D │ │ │ 02_Inference-_Summary.mp4
$ L! q0 q, a- R+ x& g# ]7 Z8 \ │ │ │ 02_Inference-_Summary.srt
, A) |: d6 t: {/ E5 D5 [4 q" K$ q │ │ │ 02_Inference-_Summary.txt
! J' P) h. ^2 m) P8 @ │ │ │
8 b, {2 P* z+ i │ │ ├─15_Decision_Theory
6 y+ d: j9 p' _, D6 S │ │ │ 01_Maximum_Expected_Utility.mp4; @1 Z1 @5 ~' r/ L# W; Q! x
│ │ │ 01_Maximum_Expected_Utility.srt
; \! v {/ f+ P. ? │ │ │ 01_Maximum_Expected_Utility.txt) q9 u' L/ q, Q2 t" H$ U
│ │ │ 02_Utility_Functions.mp43 |( d9 [3 |& ~/ F
│ │ │ 02_Utility_Functions.srt; k6 q( ~+ f: D9 p4 z
│ │ │ 02_Utility_Functions.txt
" v7 i; C& g: f# ^- N2 A# f │ │ │ 03_Value_of_Perfect_Information.mp4; ]$ e. Y$ o& t' V& K9 C2 I- V+ i# Y* `
│ │ │ 03_Value_of_Perfect_Information.srt7 _1 w$ a5 N* c
│ │ │ 03_Value_of_Perfect_Information.txt; r* l4 d2 o( r. Y7 f5 K
│ │ │ * Q$ H+ z" n9 r8 N$ g
│ │ ├─16_ML-class_Revision
. Z+ U3 d4 ?7 W, { │ │ │ 01_Regularization-_The_Problem_of_Overfitting.mp4+ R# W% Q0 ^* t; b: \
│ │ │ 01_Regularization-_The_Problem_of_Overfitting.srt# D( |) {, V O: L. H
│ │ │ 01_Regularization-_The_Problem_of_Overfitting.txt
0 P$ v4 S* b! O │ │ │ 02_Regularization-_Cost_Function.mp4$ ~; _6 |% ~' n1 S+ ^
│ │ │ 02_Regularization-_Cost_Function.srt+ W7 ]3 J/ ^' Z) F3 M
│ │ │ 02_Regularization-_Cost_Function.txt6 Y. p9 Z$ \4 n9 n
│ │ │ 03_Evaluating_a_Hypothesis.mp4
0 y5 K r: d# G5 @4 g0 Z; O │ │ │ 03_Evaluating_a_Hypothesis.srt a2 U3 F: f" |9 ~7 R
│ │ │ 03_Evaluating_a_Hypothesis.txt0 |( [* m' @ `8 x1 C
│ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.mp4. `% E# o" v: b r7 j* m5 D
│ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.srt
3 V9 K9 P. x* q+ [, K) n │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.txt3 S! I$ y+ d5 r& k
│ │ │ 05_Diagnosing_Bias_vs_Variance.mp40 C- D. a( p8 o' M3 b6 ]
│ │ │ 05_Diagnosing_Bias_vs_Variance.srt
3 t. l+ _! y) w1 q# h$ _) G │ │ │ 05_Diagnosing_Bias_vs_Variance.txt. X* v0 E H$ M% D
│ │ │ 06_Regularization_and_Bias_Variance.mp4
! k g6 L) X$ d5 \* `8 U │ │ │ 06_Regularization_and_Bias_Variance.srt U# o1 K; C& P0 h& B: _
│ │ │ 06_Regularization_and_Bias_Variance.txt; U% _) A% z5 ]7 l, a7 ^
│ │ │ 6 r D( C- M: G) f6 L
│ │ ├─17_Learning-_Overview
; w, A; w; B* J: a3 n3 {) }3 K │ │ │ 01_Learning-_Overview.mp4% k& V- x/ p& H
│ │ │ 01_Learning-_Overview.srt- @, a1 w1 c% @3 E" v! B5 }
│ │ │ 01_Learning-_Overview.txt/ O8 Q% ^; m9 K& k+ n- t5 ]2 V9 |" @
│ │ │ " J/ m1 c0 m! L& ^6 y% j
│ │ ├─18_Learning-_Parameter_Estimation_in_BNs
) ?- b8 i2 a9 i8 l0 E7 \ │ │ │ 01_Maximum_Likelihood_Estimation.mp4/ ~$ v% ~8 i! X% p6 q. M& [
│ │ │ 01_Maximum_Likelihood_Estimation.srt
7 | Q6 s% O" t. H# u4 k │ │ │ 01_Maximum_Likelihood_Estimation.txt
% p! Y0 N1 J1 q$ n0 [ │ │ │ 03_Bayesian_Estimation.mp4
0 g0 N% @% u7 U │ │ │ 03_Bayesian_Estimation.srt+ ^, A3 Q$ a; z2 n5 Y& u: z; g
│ │ │ 03_Bayesian_Estimation.txt
. {% C. C& g- D I7 i' c │ │ │ 04_Bayesian_Prediction.mp4
# f6 F8 ~' b. |6 g* |# d/ f │ │ │ 04_Bayesian_Prediction.srt
) e1 {0 v2 S7 ~: u2 D │ │ │ 04_Bayesian_Prediction.txt/ q3 f% B8 ]7 N1 e2 t6 n
│ │ │ 6 u3 P8 A/ d9 C& [+ }* L5 ?
│ │ ├─19_Learning-_Parameter_Estimation_in_MNs
( f+ }% J1 G- C% a# o' j2 S │ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.mp4
. c; z6 I6 I4 G │ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.srt- `5 U% \# n( |, R1 t( `) ]$ L( B; E
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.txt8 B( Z7 Y0 _: n l1 r1 A6 F: {9 b
│ │ │ # p4 v1 b/ Y$ A) u1 U2 H
│ │ ├─20_Structure_Learning9 `6 e" n b* h$ {! Q, W* ^' @4 I
│ │ │ 01_Structure_Learning_Overview.mp4, v- k6 }" I, E% j* M" R
│ │ │ 01_Structure_Learning_Overview.srt3 ^: F0 {" \- K& s9 a6 Q1 G& r! \/ t
│ │ │ 01_Structure_Learning_Overview.txt
) p5 w% H3 V; q │ │ │ 02_Likelihood_Scores.mp43 u/ Z' v+ t4 O* h3 j
│ │ │ 02_Likelihood_Scores.srt4 i+ [3 E8 {) o" {) T/ a
│ │ │ 02_Likelihood_Scores.txt; i7 }- `% U0 Y& z- p4 d* ^7 N
│ │ │ 03_BIC_and_Asymptotic_Consistency.mp4
! a' S$ h) w' H: H9 D │ │ │ 03_BIC_and_Asymptotic_Consistency.srt
+ \ u+ Y2 R' F$ r: N/ i; r# d │ │ │ 03_BIC_and_Asymptotic_Consistency.txt7 p# {$ K7 X8 V1 T, Q
│ │ │ 04_Bayesian_Scores.mp45 q; R& _0 O) S* j
│ │ │ 04_Bayesian_Scores.srt
% \6 ^8 X+ Q, x4 |9 }% N+ } │ │ │ 04_Bayesian_Scores.txt; {# g6 }( n. ^+ X5 }
│ │ │ 05_Learning_Tree_Structured_Networks.mp41 H/ h F# I/ W" `
│ │ │ 05_Learning_Tree_Structured_Networks.srt# N. m( Y1 `" A) p' I
│ │ │ 05_Learning_Tree_Structured_Networks.txt
5 U; V4 |+ S0 l │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.mp4
- {6 w4 [* j, O* `9 N2 b/ d2 c │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.srt- P2 l, C3 B5 Y& }. `1 w$ j
│ │ │ 06_Learning_General_Graphs-_Heuristic_Search.txt
7 t) g/ H0 S: c │ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.mp4" y' A# i Q' k5 P) X# {
│ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.srt: c" i( ]) n* ?! ?$ I
│ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.txt
% v& \# c6 j2 N4 r% U. t$ d │ │ │
6 ~ M% o- `8 N, j │ │ ├─21_Learning_With_Incomplete_Data
( [9 f, c5 O, Y/ B/ C │ │ │ 01_Learning_With_Incomplete_Data_-_Overview.mp4, Z' F" J S9 C" ?$ S
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.srt3 O7 U9 t5 g. V0 b, k3 F
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.txt
) U- z9 z, `- v* W/ j9 f6 _/ } │ │ │ 02_Expectation_Maximization_-_Intro.mp4: g4 j. D* D) E' V0 P
│ │ │ 02_Expectation_Maximization_-_Intro.srt
7 Z6 k9 _7 Y" R) b, ?* S/ u. y │ │ │ 02_Expectation_Maximization_-_Intro.txt) g( r6 Q+ n- b+ H
│ │ │ 03_Analysis_of_EM_Algorithm.mp4
9 ?$ S' ~; @7 r) f3 x2 y5 j │ │ │ 03_Analysis_of_EM_Algorithm.srt- V4 m- n8 X! S; P& f- m/ Q
│ │ │ 03_Analysis_of_EM_Algorithm.txt( @7 u0 }+ Y6 ?8 H0 N/ i
│ │ │ 04_EM_in_Practice.mp4
8 J- k* _! s! J: l5 k │ │ │ 04_EM_in_Practice.srt2 X+ c' Z" i2 y8 |# g( W/ k' ^
│ │ │ 04_EM_in_Practice.txt
* K1 i, c- ?3 ^4 j7 U, }- r! E │ │ │ 05_Latent_Variables.mp4- |6 ~7 U% Q. ^$ G9 x; f
│ │ │ 05_Latent_Variables.srt
7 ?- j9 y5 D ]* W │ │ │ 05_Latent_Variables.txt
1 u) z' o0 }, `& o2 t' B8 | │ │ │
& b7 R9 K `: G3 ?3 ]9 Y6 G! W │ │ ├─22_Learning-_Wrapup
s+ E9 ?: }6 k4 _6 i7 D" [# C │ │ │ 01_Summary-_Learning.mp4
) ]& y/ ~$ E7 C+ \ │ │ │ 01_Summary-_Learning.srt
, J. M! r" a. e ?1 W( z │ │ │ 01_Summary-_Learning.txt
' i, k8 _% @4 s9 c8 s# x, H │ │ │ % N4 `) K% ^7 L
│ │ └─23_Summary, X& |6 B/ q4 L% x
│ │ 01_Class_Summary.mp4+ a. ?& X, ]3 _7 s0 c, E3 L* H
│ │ 01_Class_Summary.srt6 A! k7 D: u4 @( Y
│ │ 01_Class_Summary.txt
, b) Q/ M$ r4 G/ B8 h │ │ 8 C! z% ?. t4 D8 n& s. O
│ └─Probabilistic Graphical Models - Stanford7 h7 }" u; T) O( R0 @
│ 1 - 1 - Welcome! (05-35).mp4
! [5 J4 H$ q) Q! e$ o/ P6 B3 \ │ 1 - 1 - Welcome! (05-35).srt
* j( |, c" i2 p* ]. l │ 1 - 2 - Overview and Motivation (19-17).mp4
! K4 |8 |. V* q: F3 @. g/ J │ 1 - 2 - Overview and Motivation (19-17).srt
" K4 d3 ~; W1 D9 {7 _ R, j! z2 E$ o+ l │ 1 - 3 - Distributions (04-56).mp4
, L! n8 X& Y z7 B9 j6 }* a │ 1 - 3 - Distributions (04-56).srt/ R; T7 P7 m5 i0 [+ }* y6 N
│ 1 - 4 - Factors (06-40).mp46 d: p5 c. q2 q) i: n0 h: G
│ 1 - 4 - Factors (06-40).srt. [; j( ]; [. R. P5 j2 B$ H( P! V
│ 10 - 4 - Clique Trees and Independence (15-21).mp4
% v( b; `. w9 [0 C │ 10 - 4 - Clique Trees and Independence (15-21).srt6 w0 c( \1 a+ J4 b
│ 10 - 5 - Clique Trees and VE (16-17).mp4+ z9 O. b0 ?6 D" x
│ 10 - 5 - Clique Trees and VE (16-17).srt# G9 T* H8 j- K3 d6 }
│ 10 - 6 - BP In Practice (15-38).mp44 U; R: A3 O, H% A# @+ _# c1 x
│ 10 - 6 - BP In Practice (15-38).srt
7 X# M. L" k2 r& w% F$ a │ 10 - 7 - Loopy BP and Message Decoding (21-42).mp4, J }4 ?) }* }! h
│ 10 - 7 - Loopy BP and Message Decoding (21-42).srt
5 j( g* d2 @! n: L │ 11 - 1 - Max Sum Message Passing (20-27).mp4
! l, ?7 J# b+ c0 Z+ y │ 11 - 1 - Max Sum Message Passing (20-27).srt9 Q* A/ u0 D% ~& C; r
│ 11 - 2 - Finding a MAP Assignment (3-57).mp4% {" |* O0 S7 {& e
│ 11 - 2 - Finding a MAP Assignment (3-57).srt
0 ~ c- ~3 J' o5 ? │ 12 - 1 - Tractable MAP Problems (15-04).mp4
% r. e {1 T0 u2 i+ d" A, f, A │ 12 - 1 - Tractable MAP Problems (15-04).srt3 S0 Q: o. y8 w* t- I. U& H
│ 13 - 1 - Simple Sampling (23-37).mp4
: ]3 m. c* u+ S. n* @+ L │ 13 - 1 - Simple Sampling (23-37).srt$ a0 b4 s/ ?4 s% S
│ 13 - 2 - Markov Chain Monte Carlo (14-18).mp4
0 @" s2 S9 ^, c0 m6 K) s# `0 K │ 13 - 2 - Markov Chain Monte Carlo (14-18).srt
Y, y1 }! v6 q9 Y( y" ~! d( v │ 13 - 3 - Using a Markov Chain (15-27).mp4
4 A# f& C# u; ?" x- t- z* g │ 13 - 3 - Using a Markov Chain (15-27).srt1 Q- K, e8 ~# l6 ~& p
│ 13 - 4 - Gibbs Sampling (19-26).mp4& o8 W* O2 {2 r+ m% A
│ 13 - 4 - Gibbs Sampling (19-26).srt3 ^# `' j! J+ A
│ 13 - 5 - Metropolis Hastings Algorithm (27-06).mp47 a6 v. b: j+ G
│ 13 - 5 - Metropolis Hastings Algorithm (27-06).srt* w. i, v. L4 t6 ~$ y9 Z/ }8 n+ H# f
│ 14 - 1 - Inference in Temporal Models (19-43).mp4
. U% H; u: s% h" g │ 14 - 1 - Inference in Temporal Models (19-43).srt8 U7 e7 q; {; m$ \) C, t: c0 M
│ 14 - 2 - Inference- Summary (12-45).mp4! R! p- a2 D$ f3 Y4 D
│ 14 - 2 - Inference- Summary (12-45).srt
- F+ K P7 g* Z% m* B! h │ 15 - 1 - Maximum Expected Utility (25-57).mp47 H( B7 u9 Q+ t& Z( B! p
│ 15 - 1 - Maximum Expected Utility (25-57).srt r6 e4 e/ I; G* J! R/ X! _6 }: _
│ 15 - 2 - Utility Functions (18-15).mp42 Z8 I/ L, H: b! Q! Y4 l
│ 15 - 2 - Utility Functions (18-15).srt6 W* w) m" g' p1 w$ c( ]
│ 15 - 3 - Value of Perfect Information (17-14).mp4+ z5 |% E" ?) C% h
│ 15 - 3 - Value of Perfect Information (17-14).srt6 T) q# F2 s) K* W1 M6 C/ p
│ 16 - 2 - Regularization- Cost Function (10-10).mp4& x4 O1 W# R& }( b1 }+ K( D2 N- O
│ 16 - 2 - Regularization- Cost Function (10-10).srt7 r' C/ T, P! a9 n5 i% y, O
│ 16 - 3 - Evaluating a Hypothesis (07-35).mp4. I u& i! Y+ u1 l" x: b g
│ 16 - 3 - Evaluating a Hypothesis (07-35).srt
( \. H4 U6 L; G; u │ 16 - 5 - Diagnosing Bias vs Variance (07-42).mp4
- a" s) H6 r& S │ 16 - 5 - Diagnosing Bias vs Variance (07-42).srt0 l) ?9 a( ?# X0 w2 J' G
│ 17 - 1 - Learning- Overview (15-35).mp4
& K4 a" `7 d# r4 H% x │ 17 - 1 - Learning- Overview (15-35).srt+ B& R1 K+ d3 U& B9 Y; D
│ 18 - 1 - Maximum Likelihood Estimation (14-59).mp41 w0 @1 M0 ?# F$ \& ], D
│ 18 - 1 - Maximum Likelihood Estimation (14-59).srt7 e- X- X7 X. K+ d$ ~2 {+ A+ K! j" l
│ 18 - 3 - Bayesian Estimation (15-27).mp4; q* w- M5 X) l) M; t; n8 s; E- w
│ 18 - 3 - Bayesian Estimation (15-27).srt
6 d% A# |+ J! K7 _9 k │ 18 - 4 - Bayesian Prediction (13-40).mp41 W6 M' ~5 ?' I t
│ 18 - 4 - Bayesian Prediction (13-40).srt
* m# u# H+ X1 O0 c$ r. q& F │ 2 - 1 - Semantics & Factorization (17-20).mp4% J' ]( J% y7 F7 ]2 y8 F) e3 e
│ 2 - 1 - Semantics & Factorization (17-20).srt8 j+ C2 |5 l: j6 a, C9 l5 V
│ 2 - 2 - Reasoning Patterns (09-59).mp48 x- t* c, {/ w4 n8 |! U
│ 2 - 2 - Reasoning Patterns (09-59).srt
/ r- f5 K" j( N& Z8 w+ t+ }" m: K │ 2 - 4 - Conditional Independence (12-38).mp4
! {; O% N. f+ ^3 N- U: J- a7 y │ 2 - 4 - Conditional Independence (12-38).srt
, V: Q2 Y' t$ x/ E │ 2 - 6 - Naive Bayes (09-52).mp4# [6 Q e7 ^. p9 x0 A
│ 2 - 6 - Naive Bayes (09-52).srt
: g8 ~ y) ^, A/ T │ 20 - 1 - Structure Learning Overview (5-49).mp47 X% ^! V' [5 N
│ 20 - 1 - Structure Learning Overview (5-49).srt ?7 q* E v$ i$ d, W
│ 20 - 2 - Likelihood Scores (16-49).mp4. v( P+ [5 K k5 l
│ 20 - 2 - Likelihood Scores (16-49).srt2 g2 }( B7 s) N* z. u* E9 ~
│ 20 - 4 - Bayesian Scores (20-35).mp4
7 u8 X% h- F9 e, N │ 20 - 4 - Bayesian Scores (20-35).srt' a- i9 o- D; ]
│ 21 - 3 - Analysis of EM Algorithm (11-32).mp4
8 r( M! o1 o/ t# b │ 21 - 3 - Analysis of EM Algorithm (11-32).srt0 b9 {5 Y: d. i' m) S9 L
│ 21 - 4 - EM in Practice (11-17).mp4
+ D1 ^6 w. l# _7 o) }- x$ p │ 21 - 4 - EM in Practice (11-17).srt2 S; {$ K) o$ j7 c
│ 21 - 5 - Latent Variables (22-00).mp4
) ^5 f4 }) ?# U/ y# G │ 21 - 5 - Latent Variables (22-00).srt
: j0 w. M- {1 K" p │ 22 - 1 - Summary- Learning (20-11).mp4: w1 V4 A/ i. A }4 U( S( h
│ 22 - 1 - Summary- Learning (20-11).srt$ n8 K7 E- a+ E, K
│ 23 - 1 - Class Summary (24-38).mp4: L3 K, R5 |* v; v N
│ 23 - 1 - Class Summary (24-38).srt- C; k: e+ I0 h$ P+ V# E. ]2 v# E a
│ 3 - 1 - Overview of Template Models (10-55).mp4
8 m! M Q8 g% I% n: C │ 3 - 1 - Overview of Template Models (10-55).srt
1 D4 [3 K) \* L& ` │ 3 - 2 - Temporal Models - DBNs (23-02).mp45 ?8 }9 y7 W& _, a" e
│ 3 - 2 - Temporal Models - DBNs (23-02).srt; ?0 v- l- D; `* m1 e
│ 3 - 3 - Temporal Models - HMMs (12-01).mp4
8 X& j3 s# w% w& S* C │ 3 - 3 - Temporal Models - HMMs (12-01).srt' _- H$ P- D8 B* K* _. E" M
│ 3 - 4 - Plate Models (20-08).mp4' \* L! n6 p) S
│ 3 - 4 - Plate Models (20-08).srt
$ S3 _. s$ A: \5 y: ?# D │ 4 - 1 - Basic Operations (13-59).mp4
* ?5 Y& j, u) [+ G, p │ 4 - 1 - Basic Operations (13-59).srt1 B f: k& l6 S" Y# `2 N
│ 4 - 2 - Moving Data Around (16-07).mp46 W2 ~+ _1 q# G* m9 N
│ 4 - 2 - Moving Data Around (16-07).srt- Y5 p- z7 q* U( P n1 t
│ 4 - 3 - Computing On Data (13-15).mp4
# H) e; Q; f' w7 B8 S8 J. ^ │ 4 - 3 - Computing On Data (13-15).srt5 H+ P8 b9 t7 b& b, U$ z" B- ] M
│ 4 - 4 - Plotting Data (09-38).mp4
3 [$ i! G$ u4 m6 a3 s5 [. }8 ] │ 4 - 4 - Plotting Data (09-38).srt& c, t" b# T8 U4 F% z5 d
│ 4 - 6 - Vectorization (13-48).mp45 @! Z+ M- n4 f. j2 ?8 L
│ 4 - 6 - Vectorization (13-48).srt
. [. p. O# e$ N9 D │ 5 - 1 - Overview- Structured CPDs (08-00).mp4
% P& X2 @) L" e │ 5 - 1 - Overview- Structured CPDs (08-00).srt
2 E) e' y T; ^ │ 5 - 2 - Tree-Structured CPDs (14-37).mp41 J* f4 x( J' M5 h
│ 5 - 2 - Tree-Structured CPDs (14-37).srt- T) u# f9 r3 e0 @
│ 5 - 4 - Continuous Variables (13-25).mp4
, A0 s9 Q+ u1 i9 S6 \0 g │ 5 - 4 - Continuous Variables (13-25).srt
5 Y+ S. R* X/ c+ A: m. L │ 6 - 1 - Pairwise Markov Networks (10-59).mp4 J. ^1 v7 y3 @- [7 v
│ 6 - 1 - Pairwise Markov Networks (10-59).srt; Z. M4 w; Y& b9 P
│ 6 - 2 - General Gibbs Distribution (15-52).mp4
9 [& j, i, T, W6 j8 Q6 i( H │ 6 - 2 - General Gibbs Distribution (15-52).srt5 N3 u0 D8 @1 n/ |6 [ w( n
│ 6 - 3 - Conditional Random Fields (22-22).mp4
0 s3 T7 V9 c9 P5 n# o │ 6 - 3 - Conditional Random Fields (22-22).srt
4 i/ Y0 e% j8 b1 c0 |5 I* f6 d, Q │ 6 - 5 - I-maps and perfect maps (20-59).mp4( J9 c# v7 O( U; I, Q5 p
│ 6 - 5 - I-maps and perfect maps (20-59).srt
0 e* l0 f4 n- \ r# T │ 6 - 6 - Log-Linear Models (22-08).mp4
4 c$ N! n- D& Y* b2 J │ 6 - 6 - Log-Linear Models (22-08).srt
) X( L6 r" R2 H9 x- q5 x8 ? │ 7 - 1 - Knowledge Engineering (23-05).mp45 V2 _& f9 L5 V4 e& l$ w4 Y4 e
│ 7 - 1 - Knowledge Engineering (23-05).srt
# b% o. S w, v' I$ l- ? │ 8 - 2 - Overview- MAP Inference (09-42).mp4
. Z/ A/ g' `0 o9 C' T │ 8 - 2 - Overview- MAP Inference (09-42).srt6 _! u2 A& j( E5 ]1 e% c- f
│ 8 - 3 - Variable Elimination Algorithm (16-17).mp4
& H% w; }6 D$ W1 b0 S& T9 M │ 8 - 3 - Variable Elimination Algorithm (16-17).srt; a8 a3 X4 R8 m" b0 O* J% {
│ 8 - 6 - Finding Elimination Orderings (11-58).mp41 N7 n" z u. f
│ 8 - 6 - Finding Elimination Orderings (11-58).srt
' |2 W' \0 I3 y# i( J │ 9 - 1 - Belief Propagation (21-21).mp49 j; w2 P3 x& @; k7 w T5 P
│ 9 - 1 - Belief Propagation (21-21).srt/ R; Z2 t; ?* ^+ {2 P: ~9 u
│ 9 - 2 - Properties of Cluster Graphs (15-00).mp4+ r; @0 h5 I% s* n u. ^
│ 9 - 2 - Properties of Cluster Graphs (15-00).srt9 M3 e. f1 K. V- c E) ~- ~" H
│ PGM-Programming_Assignment_1.zip" M0 d# V$ K" r6 {, F
│ PGM-Programming_Assignment_2.zip
3 m5 s6 e5 T) L9 L$ x │ PGM-Programming_Assignment_3.zip
6 Y' @' K% G" U; @9 U, ^0 Z9 L │ PGM-Programming_Assignment_4.zip
$ v7 i$ w7 L. U) j7 I, f8 J2 [ │ PGM-Programming_Assignment_5.zip
) Y5 p4 f _( g9 x+ J: R │ PGM-Programming_Assignment_6.zip
! p" `+ l- q. N% |/ c/ |' ~ │ PGM-Programming_Assignment_7.zip7 \1 w- N" k0 t7 v9 X8 ~% L
│ PGM-Programming_Assignment_8.zip9 g' H# ^, |9 x- ^2 A' g+ }
│ PGM-Programming_Assignment_9.zip4 _7 Q7 ^7 J3 Q, {2 W% h4 a
│ 下载说明.txt
4 d2 u8 S* T- K. x% q3 l. ^ │ 关注我们.png
4 v) ^' z5 F. e6 a* U3 y5 X) G │ 攻城狮论坛=网络技术+编程视频.url1 L+ z, ~# D; L* r0 y
│ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
# [0 ]2 s% \9 W$ x! \! c) t │
+ l/ K/ p6 b% B: \' U( L ├─031_台大概率
: E! ^# A9 Y6 N& q% h │ └─031_台大概率5 E, W# @1 S" A& N) h+ @ L) m) Y
│ ├─prob-001
- r& E. }: r. ~ S9 s0 @ │ │ ├─01_Week_1
( |' H% C$ \" ^8 j% z, } │ │ │ 01_1-0.mp4. M' ?) [, h {" f! H5 H4 }# c
│ │ │ 02_1-1_17-41.mp4
' d4 ~1 N# E& w │ │ │ 02_1-1_17-41.pdf
P3 N; b) T* a5 U6 r$ r │ │ │ 02_1-1_17-41_0_.pdf
/ m: N, ]5 w9 g. d1 A9 g │ │ │ 02_1-1_17-41_1_.pdf
5 s F( P4 `/ n6 d" Q0 d, O │ │ │ 02_1-1_17-41_2_.pdf/ a4 b( R9 @6 m) {
│ │ │ 03_1-2.a__11-46.mp47 g" D& o, G1 V6 ?$ Z
│ │ │ 04_1-2.b__09-40.mp4
1 w$ L' V6 N3 p! P1 p8 ^ │ │ │ 05_1-3.a__11-24.mp4
) o& f0 W u3 T/ a$ l% M, M# m5 w │ │ │ 06_1-3.b__16-36.mp43 Y& k, [7 X r7 X% }
│ │ │
`6 U, O8 ~- l! e; x; p5 j& f4 C& J │ │ ├─02_Week_2
' X1 j9 f" X5 m5 P$ e │ │ │ 01_2-0_15-32.mp4' C& N7 g7 a6 g0 b
│ │ │ 02_2-1.a__16-09.mp4/ k l' s1 B' W% X0 Q
│ │ │ 02_2-1.a__16-09.pdf" J3 j$ U7 E' ]; {& U$ ~
│ │ │ 02_2-1.a__16-09_0_.pdf6 j* X3 e. H7 P2 |& ^
│ │ │ 02_2-1.a__16-09_1_.pdf a1 t/ w2 ?& q, I7 w
│ │ │ 02_2-1.a__16-09_2_.pdf
( C3 P- f; Y& q! D$ P │ │ │ 03_2-1.b__16-06.mp41 T; G- `; R. v# Q
│ │ │ 04_2-1.c__10-07.mp4
$ A- C" R+ M8 |# N, W/ p( }+ C │ │ │ 05_2-2.a__10-41.mp4
; v9 C; S4 t, N5 x0 S) I │ │ │ 06_2-2.b__12-43.mp4- h: `; ~ C, N& ~4 g0 a+ X7 ^
│ │ │ 07_2-2.c__16-02.mp4
% R1 V1 ^ e# p/ l- M │ │ │
) |8 G4 i& R c │ │ ├─03_Week_34 b9 P- g/ S8 {2 C- l. ?6 k
│ │ │ 01_3-0_16-46.mp4* O! }" \4 O" l, h' k
│ │ │ 02_3-1.a___09-12.mp4
+ \$ B2 d0 R. c7 W# `" p4 n │ │ │ 02_3-1.a___09-12.pdf
* g) _. S$ W7 A! Y( a3 ` │ │ │ 02_3-1.a___09-12_0_.pdf5 X0 Y& d) |6 R: A; _5 f
│ │ │ 02_3-1.a___09-12_1_.pdf L' W' a0 [) O% q
│ │ │ 02_3-1.a___09-12_2_.pdf
* e! K. v! E3 U5 r/ I │ │ │ 03_3-1.b__10-35.mp4
. M! l6 x9 t6 E; k: T5 H │ │ │ 04_3-2_08-47.mp4
' R- i- D# u: k+ V+ D6 f1 a" j │ │ │ 05_3-3.a__16-57.mp44 J0 Q; _' U$ E! Q& X
│ │ │ 06_3-3.b__12-58.mp4
& p- j' p+ f) h$ z1 [1 g │ │ │
8 U8 ^" z3 ]6 F4 Y% _ │ │ ├─04_Week_4# L T6 V! E- O- R6 ], H
│ │ │ 01_4-0_17-33.mp4
! j# L/ J( s- G. |. z A3 P+ q │ │ │ 02_4-1.a__13-53.mp4
1 K. f& T, p, Z4 }' ]* r │ │ │ 02_4-1.a__13-53.pdf
+ W6 v5 d$ J8 Q7 \ │ │ │ 02_4-1.a__13-53_0_.pdf
! K! U2 q9 u) C5 @ C1 R │ │ │ 02_4-1.a__13-53_1_.pdf: d/ \: w& Z( U6 D) l
│ │ │ 02_4-1.a__13-53_2_.pdf
* m; Z5 a" T5 V3 Z │ │ │ 03_4-1.b__14-43.mp4' A. q* p' z$ l# w" Z3 d
│ │ │ 04_4-1.c__5-18.mp4
0 e& X, s/ }1 b) Q! X0 |0 T J( R │ │ │ 05_4-2.a_CDF__9-48.mp49 Q* t8 C! j/ U* d" g! `
│ │ │ 06_4-2.b_CDF__8-59.mp49 m0 J* t3 {8 \5 Q& T8 y* _
│ │ │ 07_4-2.c_CDF__9-00.mp4
6 F! U8 S) D6 \/ f# G/ C9 F │ │ │ 08_4-3_PMF_11-26.mp4
* ~3 N: p2 A0 ?/ c4 W+ H; m │ │ │ 09_4-4.a_I__14-41.mp4
4 V! j' \0 ?4 q1 c) M │ │ │ 10_4-4.b_I__8-47.mp4
& z. R8 x+ r) Q# o7 V │ │ │
7 {5 b* r4 k$ X% @# b │ │ ├─05_Week_5
+ I6 ^+ u; C1 {. W, \, q* e │ │ │ 01_5-0_14-09.mp4" v4 B2 e& e9 d. x$ W
│ │ │ 02_5-1.a_II__10-36.mp4& G* M! w) S/ g$ x3 n% n
│ │ │ 02_5-1.a_II__10-36.pdf7 t! | e! ]" F+ t6 |+ ?( W: l$ ]1 }
│ │ │ 02_5-1.a_II__10-36_0_.pdf$ h4 i2 S; t. G8 A% c# t" Y
│ │ │ 02_5-1.a_II__10-36_1_.pdf! O" T' m. h7 L! z# ]5 Q0 Z
│ │ │ 02_5-1.a_II__10-36_2_.pdf9 A: C& U4 ]8 F; t
│ │ │ 03_5-1.b_II__12-06.mp40 f$ r# ? {! \5 M" a
│ │ │ 04_5-1.c_II__20-28.mp4
$ w5 ^& ~0 f& V, D4 t( G1 L. ^! B i0 s │ │ │ 05_5-2_PDF_18-56.mp42 W$ ], H7 B+ @: z; _8 _
│ │ │ 06_5-3_I_18-12.mp4' \: w. a2 f) b' r4 |* D- w, f1 ~
│ │ │
% a9 U* r8 ]9 e; {$ T- t, F2 | │ │ ├─06_Week_60 O3 s; r9 }* E
│ │ │ 01_6-0_10-13.mp4
% V. s# w0 X9 r3 D8 `' Y( O │ │ │ 02_6-1.a_II__15-25.mp4" D4 f! Z; f) Z; |
│ │ │ 02_6-1.a_II__15-25.pdf J' V* V# e1 _# L: U
│ │ │ 02_6-1.a_II__15-25_0_.pdf
% x1 o5 O' l Q. W* J e │ │ │ 02_6-1.a_II__15-25_1_.pdf
3 b9 H6 X; _) h' F. p( Q8 i4 B │ │ │ 02_6-1.a_II__15-25_2_.pdf+ j6 B0 ?! N& i. C# }: g8 g* {- B
│ │ │ 03_6-1.b_II__16-08.mp45 M' ^, w T# ^! Q; x! A/ C
│ │ │ 04_6-1.c_II__17-16.mp4/ j+ M/ P* A( z9 }! W$ |4 E
│ │ │ 05_6-1.d_II__5-40.mp4
% h' ?6 Z+ P) j7 W, P9 T; ^1 A+ Z8 Q │ │ │ 06_6-2.a_I__16-35.mp4& l; U) R' A. w' m, R* s2 f, I
│ │ │ 07_6-2.b_I__10-41.mp4
* B. N6 [+ u7 t/ Y, o │ │ │ 08_6-2.c_I__16-44.mp4' Z5 l v5 U x- R
│ │ │ 09_6-2.d_I__14-30.mp4
7 f* P: _* c9 T. ]* C% b) R: a │ │ │ $ }8 I& J# F: }4 L0 l( Q. E6 r3 O
│ │ ├─07_Week_73 Z7 b/ V9 M9 w& V5 {
│ │ │ 01_7-0.mp4
9 Z0 Q* G; @: T" d' t │ │ │ 02_7-1.a_II__14-31.mp4* U* B# Z* m' b, A( j8 @
│ │ │ 02_7-1.a_II__14-31.pdf( i5 n0 W7 f5 s! E% V) h" q$ E
│ │ │ 02_7-1.a_II__14-31_0_.pdf9 k9 O, ^! ^) S6 X
│ │ │ 02_7-1.a_II__14-31_1_.pdf
7 }3 C7 H: X$ h% u# f* B │ │ │ 02_7-1.a_II__14-31_2_.pdf
+ E/ b/ X( k4 ~, j! ]3 L8 X │ │ │ 03_7-1.b_II__13-07.mp4
; U5 D# ]( t4 t5 ^$ q │ │ │ 04_7-2.a-___10-35.mp4# Y G- o* i+ n) U2 r
│ │ │ 05_7-2.b-___08-42.mp45 Y4 j; Z: m/ F! R5 K* G
│ │ │ 06_7-3.a-___15-07.mp4
S ]% D6 l5 H- a! ~ │ │ │ 07_7-3b-___19-20.mp4
& Z6 |9 U) X0 L8 Z, W* g │ │ │
: X5 k/ f% c$ ?) |5 i% F1 u │ │ ├─08_Week_8
7 _0 P; i+ F& G' h3 `! z$ q! [( a │ │ │ 01_8-0.mp4
# j; l& ?$ k1 V" V( p9 b- f │ │ │ 02_8-1.a__14-36.mp4; X$ z. g; k2 e; u! E4 t
│ │ │ 02_8-1.a__14-36.pdf( M/ S9 R; D r) f* U
│ │ │ 02_8-1.a__14-36_0_.pdf
' ?* c1 x6 y+ s, c0 A% A8 X" G2 M1 _ │ │ │ 02_8-1.a__14-36_1_.pdf
. A. `! t; {% K! a- T4 |! s │ │ │ 02_8-1.a__14-36_2_.pdf
7 J) P5 `3 Z- ? │ │ │ 03_8-1.b__15-05.mp4
5 A, A( Y7 g, b7 j0 Q │ │ │ 04_8-1.c__17-00.mp41 ~& b. Y+ G% F( v
│ │ │ 05_8-1.d__11-18.mp4
* a% I4 p$ Y1 k3 F: x2 Z7 ~ │ │ │ 06_8-2_12-32.mp4
3 F2 @2 k5 Z8 K% r# e │ │ │ 07_8-3.a__10-06.mp4
1 p0 H. \# f3 t9 w │ │ │ 08_8-3.b__17-27.mp4# s% R; X; u2 d( A! [- x
│ │ │
# W8 ~/ ?( H( G8 N# t │ │ └─09_Week_9
& [ u$ W* O- Z3 |0 F ?' |( e! ] │ │ 01_9-1.a__11-18.mp4
; N2 O$ o, H) D1 K; o: Z │ │ 01_9-1.a__11-18.pdf8 ~. p0 c! A. S- D' f4 J
│ │ 01_9-1.a__11-18_0_.pdf1 f' f* f7 s" `- d0 k0 x% e
│ │ 01_9-1.a__11-18_1_.pdf; S# E9 F; d( W5 Y5 L
│ │ 01_9-1.a__11-18_2_.pdf* Q6 C1 ]6 P, A" `3 P
│ │ 02_9-1.b__13-49.mp4# Y/ T; ?: b* S0 B+ a
│ │ 03_9-2.aMGF__10-17.mp4# `# X: k4 ~ g w
│ │ 04_9-2.bMGF__14-06.mp4
" ?7 d* t& x: ?# H( ]: f+ n │ │ 05_9-2.cMGF__15-53.mp4
* k5 p F2 m0 e' h- ^ c │ │ 06_9-3.a__10-35.mp4! T# A* ^" @: J3 ]1 c9 \5 x
│ │ 07_9-3.b__13-01.mp4$ }/ G& l/ L% y8 E
│ │ 08_9-4.a-__16-45.mp4+ k6 _4 o/ I1 Z! _$ J3 Q! V
│ │ 09_9-4.b-__17-19.mp4
3 J6 S/ S- Z9 `, y' `0 o) k │ │ . f/ S/ e5 N, P, c' I, Z: z$ v& O
│ └─概率-台大 |& Z9 {) W; ~' J' s
│ │ 02-8.R
2 O! a; M A6 H j' B, I; I+ L$ C │ │
1 @; P7 U4 p: c! b0 j0 v" x( W │ ├─1
; g% p4 F4 m: x: I2 D0 q& G │ │ Benson_Coursera_Week_1_簡.pdf+ {6 ^$ A+ F. }* \5 M' L1 X
│ │ Benson_Coursera_Week_1_繁.pdf' i! k+ r; H0 F7 D% m+ ]
│ │ Benson_Coursera_Week_1_繁空.pdf
: N" c9 t. {6 t. ^$ F8 X+ {6 N& e8 j │ │ 2 - 1 - 1-0：咱們先聊聊，這是門什麼樣的課呢-.mp4' E0 ], d% |0 S6 `) \- Q
│ │ 2 - 2 - 1-1：機率概論 (17-41).mp4
6 |4 R T! e' w) k$ t9 E; K& u │ │ 2 - 3 - 1-2.a：集合論 (上) (11-46).mp44 ]1 X2 n" y7 ~3 N1 Q, x
│ │ 2 - 4 - 1-2.b：集合論 (下) (09-40).mp4. r' d5 o4 d }8 R/ [0 q) n2 ]
│ │ 2 - 5 - 1-3.a：機率名詞 (上) (11-24).mp4( A$ d9 X; ^8 [' D- B0 k) v
│ │ 2 - 6 - 1-3.b：機率名詞 (下) (16-36).mp4
9 ^& d1 }$ U% a) m; e7 s6 Q* [1 `- c │ │ 6 V0 Y z) d, U& P3 I4 D( u
│ ├─24 G3 l! d. ~' C
│ │ Benson_Coursera_Week_2_簡.pdf, U2 }2 q2 {: {
│ │ Benson_Coursera_Week_2_簡空.pdf) [+ W* C; N! _1 {$ A* a
│ │ Benson_Coursera_Week_2_繁.pdf! {, a* J. ^! }' H/ q. u* ]
│ │ Benson_Coursera_Week_2_繁空.pdf: I6 J) c0 j, U [( p
│ │ 3 - 1 - 2-0：咱們聊聊，是學習，還是應付- (15-32).mp4
3 E& }1 ]8 t" u3 j │ │ 3 - 2 - 2-1.a：機率公理性質 (上) (16-09).mp4 L. a8 Q2 @* \$ R' z9 C7 [
│ │ 3 - 3 - 2-1.b：機率公理性質 (中) (16-06).mp40 P" i/ V Y4 L, @0 W# P7 c
│ │ 3 - 4 - 2-1.c：機率公理性質 (下) (10-07).mp41 \( y, o* U T) p
│ │ 3 - 5 - 2-2.a：條件機率 (上) (10-41).mp4- k7 F. \5 G+ ^( n0 o/ f7 ~; Y
│ │ 3 - 6 - 2-2.b：條件機率 (中) (12-43).mp4
8 @; h3 L9 M3 O" s% M% E; a │ │ 3 - 7 - 2-2.c：條件機率 (下) (16-02).mp4
# {6 _' y2 f9 x' [! r P │ │ + F3 {" r6 x" H/ z2 B: e$ @
│ ├─3
+ {0 \7 N; D% a; W │ │ Benson_Coursera_Week_3_簡.pdf
* k- D% v% _: D$ i F │ │ Benson_Coursera_Week_3_簡空.pdf
* C! W4 ~; \' T2 q% `9 g- _& u! y │ │ Benson_Coursera_Week_3_繁.pdf4 ~7 G U, w* C1 J
│ │ Benson_Coursera_Week_3_繁空.pdf
0 [! e' c* l& ~- w+ S9 O │ │ 4 - 1 - 3-0：咱們聊聊，常見的錯誤？學習的關鍵？ (16-46).mp48 J% X+ \& O! _4 W
│ │ 4 - 2 - 3-1.a：機率的獨立性 (上) (09-12).mp4" g' I [% D) `9 e
│ │ 4 - 3 - 3-1.b：機率的獨立性 (下) (10-35).mp4/ b2 L( |, h" k, ?( J \5 x7 H
│ │ 4 - 4 - 3-2：圖解繁複機率 (08-47).mp4
0 M+ l# b8 T9 _+ r1 V! m5 i! D& P │ │ 4 - 5 - 3-3.a：數數算機率 (上) (16-57).mp4" ^2 q/ H6 B" ^- U; e
│ │ 4 - 6 - 3-3.b：數數算機率 (下) (12-58).mp4
: F/ D5 r( P) d9 r- T$ Y │ │ $ ? W) _/ ~4 C! q9 w+ s8 }, g
│ ├─4) _+ J& @( H& T: @4 k! e6 n: {3 x) B
│ │ Benson_Coursera_Week_4_簡.pdf
( C. ?) S' c- r7 {9 S9 G( ^* |/ T │ │ Benson_Coursera_Week_4_簡空.pdf
1 ?6 `8 N7 n) p- n( T) i( H │ │ Benson_Coursera_Week_4_繁.pdf. W6 a3 ]( M0 T- g" k8 e0 I
│ │ Benson_Coursera_Week_4_繁空.pdf- M) R$ E! X: m! _8 @! V
│ │ 5 - 1 - 4-0：咱們聊聊，如何幫自己面對未來挑戰？ (17-33).mp4
4 R4 B$ l0 P4 N' G$ P, R │ │ 5 - 10 - 4-4.b：離散機率分佈 I (下) (8-47).mp4
0 j6 N3 A# Y. |8 m( @* ^ │ │ 5 - 2 - 4-1.a：隨機變數 (上) (13-53).mp4
# r5 P1 N7 j) d! U* z1 M% p8 } │ │ 5 - 3 - 4-1.b：隨機變數 (中) (14-43).mp4
M: g& ?& p v │ │ 5 - 4 - 4-1.c：隨機變數 (下) (5-18).mp4# f- j+ | X+ ?9 d# B: K1 X4 r2 {" c
│ │ 5 - 5 - 4-2.a：累績分佈函數 CDF (上) (9-48).mp4" x) z8 M$ j. M" S
│ │ 5 - 6 - 4-2.b：累績分佈函數 CDF (中) (8-59).mp4$ Q3 o$ T8 O2 j3 ?
│ │ 5 - 7 - 4-2.c：累績分佈函數 CDF (下) (9-00).mp4
! I# F+ r! `* T7 w' D( p │ │ 5 - 8 - 4-3：機率質量函數 PMF (11-26).mp4
9 j, ?0 c4 g* f+ k9 z" @8 ^ │ │ 5 - 9 - 4-4.a：離散機率分佈 I (上) (14-41).mp4
2 R- m% f% G% x, Z. S │ │ , m- r" W" G* o2 F
│ ├─5
. s4 |2 x/ x) _ │ │ Benson_Coursera_Week_5_簡.pdf
6 @ J/ B! D6 S/ a4 v- A │ │ Benson_Coursera_Week_5_簡空.pdf
9 f% N/ @# n4 m/ e+ Y; C* L │ │ Benson_Coursera_Week_5_繁.pdf
7 W0 h6 \) O: w/ j3 [ v$ X' N │ │ Benson_Coursera_Week_5_繁空.pdf O$ I8 E6 {. q$ |. s3 n' x- ?
│ │ 6 - 1 - 5-0：咱們聊聊，願你夜夜好眠到天明！ (14-09).mp4) l9 O, I; p- r! [
│ │ 6 - 2 - 5-1.a：離散機率分佈 II (上) (10-36).mp45 b; E; `: _- O8 u5 c6 ]- ]% ~
│ │ 6 - 3 - 5-1.b：離散機率分佈 II (中) (12-06).mp4
5 ^' `6 z4 o6 }9 k6 A │ │ 6 - 4 - 5-1.c：離散機率分佈 II (下) (20-28).mp4
' B( {; s$ H: O! h. c' v; s │ │ 6 - 5 - 5-2：機率密度函數 PDF (18-56).mp4. [8 Z. s8 f) v f
│ │ 6 - 6 - 5-3：連續機率分佈 I (18-12).mp4 t4 i1 Q) V( [3 V5 G' \! f7 }4 u
│ │ - l @0 B8 ?/ |) E& u; M* {
│ ├─6
; |. @) B/ D: q* P& e" t0 V. h │ │ Benson_Coursera_Week_6_簡.pdf; J* j, u2 c' l
│ │ Benson_Coursera_Week_6_簡空.pdf9 c# ~7 V7 L$ l
│ │ Benson_Coursera_Week_6_繁.pdf
2 Q8 U5 {0 R9 I9 w │ │ Benson_Coursera_Week_6_繁空.pdf
+ A! Z5 F) {! o# d, | │ │ 7 - 1 - 6-0：咱們聊聊，成功者的條件是什麼？ (10-13).mp4
, w' v1 E- x& h& W; r& l │ │ 7 - 2 - 6-1.a：連續機率分佈 II (上) (15-25).mp4* G5 S3 A( {* T; H
│ │ 7 - 3 - 6-1.b：連續機率分佈 II (中) (16-08).mp4
& h9 a& C+ x Y9 r6 G: i │ │ 7 - 4 - 6-1.c：連續機率分佈 II (下) (17-16).mp4
2 u9 {$ e! j) Y; F6 o( c │ │ 7 - 5 - 6-1.d：連續機率分佈 II (末) (5-40).mp45 o& s7 O2 m b/ n1 |! {3 y" W
│ │ 7 - 6 - 6-2.a：期望值 I (上) (16-35).mp4
% v/ S4 Y% w. O3 u │ │ 7 - 7 - 6-2.b：期望值 I (中) (10-41).mp4
& k, L. i; |4 p │ │ 7 - 8 - 6-2.c：期望值 I (下) (16-44).mp44 z" u9 w4 D( n O! z$ }. x/ c* x
│ │ 7 - 9 - 6-2.d：期望值 I (末) (14-30).mp4
5 X# Z+ o2 m s' k4 R8 c) t │ │ $ m# i; ^5 v4 J! C* ^. H
│ ├─7 C# V' b1 `" l3 H
│ │ Benson_Coursera_Week_7_簡.pdf
# \$ Q/ b: q" n( l- b │ │ Benson_Coursera_Week_7_簡空.pdf& {1 `" L! t! d" u' o) n& t3 b
│ │ Benson_Coursera_Week_7_繁.pdf l4 R, o, r3 H( y/ `
│ │ Benson_Coursera_Week_7_繁空.pdf2 c0 l) A2 ^) n
│ │ 8 - 1 - 7-0：咱們聊聊，每天都在忙，忙的有用嗎？.mp4
' _$ F6 b, ~! u2 V' D3 v/ c │ │ 8 - 2 - 7-1.a：期望值 II (上) (14-31).mp4$ @( N) ~0 S- S
│ │ 8 - 3 - 7-1.b：期望值 II (下) (13-07).mp4: o) a8 g; H, T8 _8 D* d" z+ `
│ │ 8 - 4 - 7-2.a- 隨機變數之函數 (上) (10-35).mp4 R% @7 f, b2 M
│ │ 8 - 5 - 7-2.b- 隨機變數之函數 (下) (08-42).mp4
5 [& \- ~: g- w% a! |8 N, I* V │ │ 8 - 6 - 7-3.a- 條件機率分佈與失憶性 (上) (15-07).mp4
3 k8 ]7 e3 K( K0 A │ │ 8 - 7 - 7-3b- 條件機率分佈與失憶性 (下) (19-20).mp49 E; H- @3 ~1 _- C% y
│ │
$ d) P& J; X5 y8 L% u │ ├─8
" n. ?4 u6 J+ t+ @% c9 m │ │ Benson_Coursera_Week_8_簡.pdf
6 m3 r, `( s6 s7 F& v$ T9 k │ │ Benson_Coursera_Week_8_簡空.pdf f, b) I/ m! R0 \
│ │ Benson_Coursera_Week_8_繁.pdf! \5 P6 K h( [
│ │ Benson_Coursera_Week_8_繁空.pdf: ~4 G5 ~ T. G2 X* M
│ │ 9 - 1 - 8-0：咱們聊聊，如何探索有意義的人生？.mp4
& O6 Z. Z' _5 q6 K* [, r- x │ │ 9 - 2 - 8-1.a：聯合機率分佈 (上) (14-36).mp4
9 ^3 c" N# r/ o1 O) h& t │ │ 9 - 3 - 8-1.b：聯合機率分佈 (中) (15-05).mp4
) y" b# |- f5 o │ │ 9 - 4 - 8-1.c：聯合機率分佈 (下) (17-00).mp4
" j( V+ z& ]1 [! S- M0 e- P+ c │ │ 9 - 5 - 8-1.d：聯合機率分佈 (末) (11-18).mp4) s2 Y7 @! K% K( d5 q% o0 Z Y
│ │ 9 - 6 - 8-2：邊際機率分佈 (12-32).mp4
1 I5 @ I7 w4 z, d& _% V │ │ 9 - 7 - 8-3.a：雙變數期望值 (上) (10-06).mp42 ~) D& w3 C. N# H2 `- t( T
│ │ 9 - 8 - 8-3.b：雙變數期望值 (下) (17-27).mp4
. C3 W( _1 a% e │ │ : U/ X$ S8 L: j) P" ?3 v1 R* k
│ └─9
- S- d2 P, k4 A7 C+ Q4 u │ Benson_Coursera_Week_9_簡.pdf
) \" T, U0 `. R/ S6 X │ Benson_Coursera_Week_9_簡空.pdf
Y0 E$ O: K1 n. C8 d! K │ Benson_Coursera_Week_9_繁.pdf
' _0 R/ j3 I, L2 _ │ Benson_Coursera_Week_9_繁空.pdf5 }- J! P9 r, m& K
│ 10 - 1 - 9-1.a：隨機變數之和 (上) (11-18).mp4
3 ?1 R7 O- q. w; p+ o │ 10 - 2 - 9-1.b：隨機變數之和 (下) (13-49).mp4
( l$ D) z/ ?3 |8 [$ Z6 R8 S, I │ 10 - 3 - 9-2.a：MGF (上) (10-17).mp4
8 k6 E1 S& N8 c# p- G │ 10 - 4 - 9-2.b：MGF (中) (14-06).mp45 S8 q/ W w0 {& s* d/ H
│ 10 - 5 - 9-2.c：MGF (下) (15-53).mp4
- _* ^ |6 x- Q3 Q4 l' } │ 10 - 6 - 9-3.a：多個隨機變數和 (上) (10-35).mp4
. s9 Q- L2 y% Y │ 10 - 7 - 9-3.b：多個隨機變數和 (下) (13-01).mp41 K& n, y- |; E2 B0 P
│ 10 - 8 - 9-4.a：中央極限定理-萬佛朝宗 (上) (16-45).mp4
; n& s! g1 {4 A& g: Q4 a( y │ 10 - 9 - 9-4.b：中央極限定理-萬佛朝宗 (下) (17-19).mp4
9 M/ N5 l1 R: {$ E6 G5 }- f │
* ]6 n7 J7 b: Y3 a4 H ├─036_Coding the Matrix" k3 ]" s+ v T; N
│ └─036_Coding the Matrix
( ]' m3 P$ b- L7 A: J& ` │ └─matrix-0020 o; L( d/ u/ m! ?. `+ e
│ │ 下载说明.txt" `, y. @- N. \0 O7 @8 J
│ │ 关注我们.png! d) f; L1 s# V1 {+ h- Q c
│ │ 攻城狮论坛=网络技术+编程视频.url
5 `1 ?) o' z/ [7 y7 \9 p$ K │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt; @! t5 J; b! F- S
│ │ 0 A% Y2 o/ s4 N" N1 h3 x( q) [
│ ├─01_Week_0-_The_Function_and_the_Field# D1 R8 L% A, Q& i
│ │ 01_Course_Introduction_Part_1__9-53.mp4
% J& t) p; P8 @ │ │ 01_Course_Introduction_Part_1__9-53.srt! }. r& a# \+ w, t( g& m
│ │ 01_Course_Introduction_Part_1__9-53.txt' U5 |4 |' ~0 \* H/ u% {8 f! I$ T, S
│ │ 02_Course_Introduction_Part_2__8-49.mp4
& }4 w. u. E C, d5 m │ │ 02_Course_Introduction_Part_2__8-49.srt
- W3 \; x* I+ h8 M8 T0 c │ │ 02_Course_Introduction_Part_2__8-49.txt- ?$ \" o0 B6 Z/ V7 X( F
│ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.mp4
5 ]0 d2 X( t+ l5 p │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.pdf
) @. n! A" C4 f& R, U3 n2 t3 w │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.srt9 ^: v. t" Z) f
│ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.txt' s2 G0 F, N9 q- P/ l/ d
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.mp4
7 G! v h! p) u* h# K& X │ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.pdf1 x7 b- H. p \: _
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.srt3 N3 `- T1 o' w5 F; ^ N( N
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.txt
& q5 l$ m& R4 M; T │ │ 05_The_Field-_Playing_with_C_15-19.mp43 ~! ~6 [4 }8 r# l( [ P3 @
│ │ 05_The_Field-_Playing_with_C_15-19.pdf
3 g+ i/ F- j: G5 k: J# R │ │ 05_The_Field-_Playing_with_C_15-19.srt- ^- T9 L: r B) v/ {. P" F
│ │ 05_The_Field-_Playing_with_C_15-19.txt
; Y1 T) Y& s# E& s │ │ 06_The_Field-_Playing_with_GF2__10-28.mp4; p j+ O8 ^, c0 w
│ │ 06_The_Field-_Playing_with_GF2__10-28.pdf
" s) h* L/ t" R: z4 F* I& ? │ │ 06_The_Field-_Playing_with_GF2__10-28.srt
' n+ V6 S" t, h; J │ │ 06_The_Field-_Playing_with_GF2__10-28.txt5 J( o- K* _; m6 z. v
│ │
3 H7 `, n( l1 w │ ├─02_Week_1-_The_Vector
1 F8 p) J4 N" e! B* b% \ │ │ 01_The_Vector-_What_is_a_vector__8-20.mp4; l- n) V2 k$ n" f; V& ~$ P
│ │ 01_The_Vector-_What_is_a_vector__8-20.pdf
6 X& X$ t- _( w6 K C │ │ 01_The_Vector-_What_is_a_vector__8-20.srt
% {9 ]8 N5 w, K/ y5 N" \/ x! T │ │ 01_The_Vector-_What_is_a_vector__8-20.txt# @) ^' D! v; \
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.mp48 L* Y# A: x% F/ I/ @
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.pdf, m" x1 D' s8 a# J0 ]# h
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.srt
# |8 q2 x1 K: q) L5 r A │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.txt. ~2 Y3 e3 y7 R+ ^$ U* j: L2 a( b1 O
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.mp43 F4 X* Y/ G7 s& U5 d
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.pdf8 b1 e: T6 w2 _* r4 H5 @) I& C! p
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.srt% `5 P1 }) e2 [1 B9 [+ W, t Y* B1 B
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.txt' E. B4 ]' z( @( K% ~/ G( q
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.mp4. N% v. s7 V V0 a2 }- W. ^
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.pdf
+ O( @* S# r2 f( N* W* n │ │ 04_The_Vector-_Vectors_over_GF2__9-18.srt) H( d2 @0 l- r/ N, J$ \9 P
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.txt1 Q* i! p! g; e8 Q0 R8 p
│ │ 05_The_Vector-_Dot-product__8-49.mp4
( c; [( l2 j' F/ a4 o │ │ 05_The_Vector-_Dot-product__8-49.pdf4 I4 @ n4 l* [
│ │ 05_The_Vector-_Dot-product__8-49.srt; T- K# G1 f: G
│ │ 05_The_Vector-_Dot-product__8-49.txt6 h4 m5 k2 B$ U9 W, w
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.mp45 ]7 q, l% P) m8 k9 m4 A- e
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.pdf- d* V3 ~& w4 G1 {
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.srt. e' Y4 p# X& J7 P( |3 R
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.txt" m% h9 O8 E; C" g5 P# K: q
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.mp4' E8 ^% V, u) B# s N
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.pdf( ]8 N6 u( }4 F$ Q _! z3 l' o
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.srt0 F) T0 k+ r! x4 o6 W- h: ?& n
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.txt
. Y% N, Y. v7 { p │ │ # f4 q5 u5 |! ^
│ ├─03_Week_2-_The_Vector_Space/ y: O, k+ W( R6 q2 V
│ │ 01_The_Vector_Space-_Linear_combinations.mp4
8 f# n8 k3 \6 p+ @# G8 H! w2 Z4 [' u │ │ 01_The_Vector_Space-_Linear_combinations.pdf2 ^* y5 b4 ^8 {- u! b! n( a
│ │ 01_The_Vector_Space-_Linear_combinations.srt6 y4 r; C+ O) _# @. i3 P2 p( D. T9 I
│ │ 01_The_Vector_Space-_Linear_combinations.txt
8 B+ Z! H/ m0 J E: V │ │ 02_The_Vector_Space-_Span.mp4: i& N+ T; f$ p
│ │ 02_The_Vector_Space-_Span.pdf7 y' T: x1 N7 N0 p* g9 ^; e
│ │ 02_The_Vector_Space-_Span.srt+ a# F: G; A$ h a1 V R/ Z* j
│ │ 02_The_Vector_Space-_Span.txt
- ?3 q5 |5 ~' s' [3 s5 x │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.mp4
: Y# h7 N2 _; r │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.pdf
# \% I: p7 l6 ]- p │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.srt& X5 E0 H( s: I/ v
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.txt* }6 I) I d4 [0 y. O" F; l$ N% Q
│ │ 04_The_Vector_Space-_Vector_spaces.mp4
, Z! N. u9 x: h! Y; r1 R │ │ 04_The_Vector_Space-_Vector_spaces.pdf B* Y1 \8 M4 M# g% p9 D
│ │ 04_The_Vector_Space-_Vector_spaces.srt( c8 W) V" l8 I1 k0 b
│ │ 04_The_Vector_Space-_Vector_spaces.txt
) e! k* j: l- m- j# i. j3 t │ │ 05_The_Vector_Space-_Checksum_function.mp4
8 Z0 ]2 m# O5 A7 E1 E( n │ │ 05_The_Vector_Space-_Checksum_function.pdf
) [+ h) r' g/ |5 Q. i$ u │ │ 05_The_Vector_Space-_Checksum_function.srt; h( X9 o8 y( W1 ?/ Z
│ │ 05_The_Vector_Space-_Checksum_function.txt
3 T: L$ W4 c. A! d5 ~9 R1 ~, O │ │ ' g* n" F* u# x' b; z! y5 ^" h: p C+ ^& A
│ ├─04_Week_3-_The_Matrix
9 R' h/ o1 O# e" g │ │ 01_The_Matrix-_What_is_a_matrix.mp45 g5 @/ F+ g; z: j: o
│ │ 01_The_Matrix-_What_is_a_matrix.pdf
/ G2 i. `; \( H/ b │ │ 01_The_Matrix-_What_is_a_matrix.srt! J3 C& V& U, ?
│ │ 01_The_Matrix-_What_is_a_matrix.txt" V7 _' g- n9 n& U& F/ Q6 p
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.mp4
+ y* ] I+ g8 Y6 ]9 g5 N# o0 a │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.pdf
8 A+ Y' c' f) }/ c) c │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.srt
+ @/ U t! m: f" T l │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.txt
# m8 }9 {5 D: g( z: I0 F │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.mp4# y# Y9 H0 K7 c1 X
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.pdf
; c. D% w1 ?- W; A5 D │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.srt
# L ]; K! q5 @3 Q │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.txt
: X4 B" [, [$ [* }$ t B& P: E │ │ 04_The_Matrix-_Null_space.mp4
8 P* ?- s* `/ D5 g( ^* O │ │ 04_The_Matrix-_Null_space.pdf
% u. e# n6 J0 Z3 p0 ^ │ │ 04_The_Matrix-_Null_space.srt
8 r1 @: P1 R* ~, [5 j" H) n* K4 I; v │ │ 04_The_Matrix-_Null_space.txt: Q9 D- T: r) |3 P; s. o
│ │ 05_The_Matrix-_Error-correcting_codes.mp4
2 J3 O9 N6 T) s! i │ │ 05_The_Matrix-_Error-correcting_codes.pdf+ M, H! `9 |6 C* |
│ │ 05_The_Matrix-_Error-correcting_codes.srt
* ?8 c; C" e' Y% H4 S │ │ 05_The_Matrix-_Error-correcting_codes.txt
# q& T4 a- R" c+ ]& m │ │ 06_The_Matrix-_Matrices_and_their_functions.mp4
( n! {+ F! z/ f │ │ 06_The_Matrix-_Matrices_and_their_functions.pdf
) I* m+ |9 W8 L, X4 N/ z: r │ │ 06_The_Matrix-_Matrices_and_their_functions.srt4 Z. u4 D8 D- v7 r; Z; K
│ │ 06_The_Matrix-_Matrices_and_their_functions.txt+ Q7 q _( |5 |, F4 S5 f
│ │ 07_The_Matrix-_Linear_functions.mp4+ [ o. A3 O( G' m* O4 R
│ │ 07_The_Matrix-_Linear_functions.pdf
6 b7 a/ A% e! A+ _8 X( n( C │ │ 07_The_Matrix-_Linear_functions.srt1 J) A2 B. s, L
│ │ 07_The_Matrix-_Linear_functions.txt
. \& _* V8 r& Q& w# B6 d │ │ 08_The_Matrix-_Matrix-matrix_multiplication.mp4! B1 M7 Q4 g1 [
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.pdf
' V& s9 P q2 {4 [, g │ │ 08_The_Matrix-_Matrix-matrix_multiplication.srt9 Z- F" c3 _6 X$ Z- X; L6 R
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.txt
, ~ U" s( S, C+ g. B8 Q4 H. h$ T │ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.mp4" U$ u" x% U. u$ |. }6 v( k
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.pdf, G, q# x. A7 ~" e
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.srt
9 X8 P2 t6 }- T8 V$ ?# I3 l │ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.txt# B( w+ z4 p. Y5 Q. M! q
│ │ 10_The_Matrix-_Matrix_inverse.mp4
! ^7 l) j. p# R% ^ │ │ 10_The_Matrix-_Matrix_inverse.pdf
- H7 M+ O" V9 c( | │ │ 10_The_Matrix-_Matrix_inverse.srt5 n9 W8 t- N) W; Q. f/ `
│ │ 10_The_Matrix-_Matrix_inverse.txt" i+ z+ {, z, U, ]0 m8 f6 D
│ │
5 s+ ^( { d# c( |6 N; ]& @7 R │ ├─05_Week_4-_The_Basis- G4 }; ^$ O" C" n2 ^# i
│ │ 01_The_Basis-_Coordinate_systems.mp4
) ]4 z+ Y3 |1 B3 s │ │ 01_The_Basis-_Coordinate_systems.pdf
: e! O9 q* N, m │ │ 01_The_Basis-_Coordinate_systems.srt8 f9 t# J2 E7 u8 Z0 i1 z) O* v
│ │ 01_The_Basis-_Coordinate_systems.txt0 S: g: _7 W; r- S0 y
│ │ 02_The_Basis-_Lossy_compression.mp4
! j7 e% u# ^/ m! x( r& x" X# V4 Q │ │ 02_The_Basis-_Lossy_compression.pdf
! J G- L9 b1 B! `: d, i │ │ 02_The_Basis-_Lossy_compression.srt6 `- \1 M) y2 e, ?4 s8 l% A* T
│ │ 02_The_Basis-_Lossy_compression.txt# }9 Q0 U) ^% l1 G8 N4 f
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.mp4
5 p0 Z8 ^% R: x │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.pdf. J3 o N5 J9 ^' @; \9 k
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.srt% f. T* k# `& z' c! k
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.txt
) |9 W, p( }: [( z T' P │ │ 04_The_Basis-_Minimum_spanning_forest.mp4
; V; G( E% \8 U3 H/ M6 o7 C3 j3 [ │ │ 04_The_Basis-_Minimum_spanning_forest.pdf! g8 p/ ?% p+ S: [7 h5 a, f
│ │ 04_The_Basis-_Minimum_spanning_forest.srt# v8 g. }! |, u" w
│ │ 04_The_Basis-_Minimum_spanning_forest.txt
/ P- I& _$ C( M N │ │ 05_The_Basis-_Linear_dependence.mp4
o1 Z( Y9 {3 U }8 B │ │ 05_The_Basis-_Linear_dependence.pdf
0 [7 }! j' b* J │ │ 05_The_Basis-_Linear_dependence.srt
) t4 q+ H% z4 o3 x4 ~/ v1 Z │ │ 05_The_Basis-_Linear_dependence.txt
% k! Z" U% G: {& B4 [& u9 ?6 L& ^ │ │ 06_The_Basis-_Basis.mp4
8 G3 m7 v7 k* s- w) X( A │ │ 06_The_Basis-_Basis.pdf5 M1 D# a r2 t7 J3 V8 `; R
│ │ 06_The_Basis-_Basis.srt
8 ]1 M5 }6 O3 t' n9 @7 ^ │ │ 06_The_Basis-_Basis.txt
' V" [* N3 }, c/ T \ │ │ 07_The_Basis-_Unique_representation.mp4
7 M2 [5 f& Z4 L% z; r0 [" F │ │ 07_The_Basis-_Unique_representation.pdf
2 r6 _+ g$ d! u │ │ 07_The_Basis-_Unique_representation.srt4 G+ ~ w& W/ S5 \' g/ a6 e% g3 b# H
│ │ 07_The_Basis-_Unique_representation.txt* a- [) W" r3 Z: n, v- B
│ │ 08_The_Basis-_Change_of_basis.mp4+ ^5 Z) E, G% D5 c: X* `
│ │ 08_The_Basis-_Change_of_basis.pdf
# u; |) _, [& [& M& N │ │ 08_The_Basis-_Change_of_basis.srt
6 d3 Y" {5 W; A │ │ 08_The_Basis-_Change_of_basis.txt! C# l% _8 F( n/ `' O
│ │ 09_The_Basis-_Perspective_rendering.mp4
n) f( e6 X% U2 }; b# [9 q │ │ 09_The_Basis-_Perspective_rendering.pdf
$ G9 Y$ a* w9 m- ~ │ │ 09_The_Basis-_Perspective_rendering.srt
: M0 _ w; {+ ]. s# F2 y8 T │ │ 09_The_Basis-_Perspective_rendering.txt
& ^( g7 ]: s$ {+ @" p- O* ] │ │ 10_The_Basis-_Perspective_rectification.mp4
' z/ E3 k! A6 S3 {. E! O │ │ 10_The_Basis-_Perspective_rectification.pdf1 `4 T6 G6 M8 k( y" P, \3 W1 g
│ │ 10_The_Basis-_Perspective_rectification.srt
7 p" v, Y, @9 W% {, p% k │ │ 10_The_Basis-_Perspective_rectification.txt
7 }5 v" G0 G" h. \0 H* z# ~7 p │ │ 11_The_Basis-_The_Exchange_Lemma.mp42 R2 O0 j @; w# Q/ A% v+ g
│ │ 11_The_Basis-_The_Exchange_Lemma.pdf
6 w- r. u. e* o1 A │ │ 11_The_Basis-_The_Exchange_Lemma.srt
: U. A! R" e! I │ │ 11_The_Basis-_The_Exchange_Lemma.txt) u0 t3 b% b9 L6 N
│ │ ' Q$ f; a9 G; ?/ T$ v" ]; `
│ ├─06_Week_5-_Dimension* Z# R, i# x2 u7 E2 D- U
│ │ 01_Dimension-_The_size_of_a_basis.mp4
4 K: b3 u# p) w! X* o │ │ 01_Dimension-_The_size_of_a_basis.pdf
6 ]. v; h0 d1 m! c! E l │ │ 01_Dimension-_The_size_of_a_basis.srt
. K P Y$ ]( i# Z9 [5 g2 q │ │ 01_Dimension-_The_size_of_a_basis.txt- X+ V' d' h7 U0 A% z( c# `, ~2 `
│ │ 02_Dimension-_Dimension_and_rank_I.mp4! c9 H H, f2 s5 e
│ │ 02_Dimension-_Dimension_and_rank_I.pdf! k k% t1 [5 L7 l1 K. {
│ │ 02_Dimension-_Dimension_and_rank_I.srt- S$ k4 H' M) L# x/ Q
│ │ 02_Dimension-_Dimension_and_rank_I.txt
: a& y: ?# q, X8 E& h% ? │ │ 03_Dimension-_Dimension_and_rank_II.mp4
3 t8 o; h6 ?9 c' ^ │ │ 03_Dimension-_Dimension_and_rank_II.pdf, D* M# [# o$ V/ z$ B) b# T
│ │ 03_Dimension-_Dimension_and_rank_II.srt9 F4 e; L8 {+ |# Z
│ │ 03_Dimension-_Dimension_and_rank_II.txt9 m3 g& Z$ i8 o4 d2 ]( C
│ │ 04_Dimension-_Direct_sum.mp4
) ^9 U1 W! s/ J+ A% L! P% ?6 a │ │ 04_Dimension-_Direct_sum.pdf
2 j/ u; E7 j% K% x2 v% D* f0 m: v │ │ 04_Dimension-_Direct_sum.srt
1 w ]3 j3 h% A% N( T │ │ 04_Dimension-_Direct_sum.txt
H# C! K0 Y3 ]7 e8 ^3 S) M │ │ 05_Dimension-_Dimension_and_linear_functions_I.mp4
9 m2 o9 k, [$ y$ H; M, l+ H │ │ 05_Dimension-_Dimension_and_linear_functions_I.pdf- c L4 F, z/ s. {4 l
│ │ 05_Dimension-_Dimension_and_linear_functions_I.srt7 A- Q8 r/ ?' O/ H
│ │ 05_Dimension-_Dimension_and_linear_functions_I.txt
+ u4 C. ?8 z0 t$ t3 u# t7 _ │ │ 06_Dimension-_Dimension_and_linear_functions_II.mp47 X1 Z/ M8 i J3 Q6 C- r& H: `3 a
│ │ 06_Dimension-_Dimension_and_linear_functions_II.pdf
8 k& R+ E! I: R6 F! t1 d9 p% x │ │ 06_Dimension-_Dimension_and_linear_functions_II.srt
/ a% V4 {- m+ a& Z │ │ 06_Dimension-_Dimension_and_linear_functions_II.txt& i; {* E* Z( S! x3 e, Q
│ │ 07_Dimension-_Two_representations_of_vector_spaces.mp4
" ^9 `2 y6 v% c Z( \# i# m- A7 I │ │ 07_Dimension-_Two_representations_of_vector_spaces.pdf- s; L, F0 Z9 Z6 B! H
│ │ 07_Dimension-_Two_representations_of_vector_spaces.srt2 _% z) b2 I- V: \
│ │ 07_Dimension-_Two_representations_of_vector_spaces.txt
/ L3 Q: E1 U9 P% R8 b0 l │ │ 08_Dimension-_Threshold_secret_sharing.mp4
2 N" h$ {, o! B1 @: J; o1 }3 t │ │ 08_Dimension-_Threshold_secret_sharing.pdf6 y6 D+ I$ y5 p# p
│ │ 08_Dimension-_Threshold_secret_sharing.srt
9 h3 a, |/ E1 c8 a │ │ 08_Dimension-_Threshold_secret_sharing.txt: F# r2 m0 t5 l, v& A: P4 _
│ │ - Z" |% W' M6 C+ b
│ ├─07_Week_6-_Gaussian_Elimination_and_the_Inner_Product+ i- h( n$ g& e% i& S8 e# m% B( r
│ │ 01_Gaussian_Elimination-_Echelon_form.mp4
& M7 V( X; g1 S& K( F │ │ 01_Gaussian_Elimination-_Echelon_form.pdf8 |: s7 F* ~2 H
│ │ 01_Gaussian_Elimination-_Echelon_form.srt9 W5 H& b9 r6 N/ }+ A7 U+ \
│ │ 01_Gaussian_Elimination-_Echelon_form.txt1 `! D& g4 O: u- o/ p- T
│ │ 04_Gaussian_Elimination-_Factoring_integers.mp4
% [9 Q; Q$ a# V- @( Q │ │ 04_Gaussian_Elimination-_Factoring_integers.pdf/ J0 o. s, t$ T B2 p' y5 N6 T' G
│ │ 04_Gaussian_Elimination-_Factoring_integers.srt0 U! v% t% [) _( W2 K/ F
│ │ 04_Gaussian_Elimination-_Factoring_integers.txt- _% Z, f, b# I7 j" j
│ │ 05_The_Inner_Product-_The_inner_product.mp46 E. B) F% F9 Q6 J; _9 g( G
│ │ 05_The_Inner_Product-_The_inner_product.pdf9 {& t5 r9 G. y. E* A) l, F
│ │ 05_The_Inner_Product-_The_inner_product.srt
v* G( Q1 V- Y- S │ │ 05_The_Inner_Product-_The_inner_product.txt
% E7 x. i: k" w" O7 T1 Q │ │ 06_The_Inner_Product-_Orthogonality.mp4
& T! c1 F, ]6 n7 i/ w, ~! I$ f │ │ 06_The_Inner_Product-_Orthogonality.pdf- L( e A/ o3 U
│ │ 06_The_Inner_Product-_Orthogonality.srt
+ e, H/ g! U6 {3 a& ^ │ │ 06_The_Inner_Product-_Orthogonality.txt. z& B- y, s! Q6 C, v
│ │
7 \ K; P9 s) V8 J6 a, T( i │ ├─08_Week_7-_Orthogonalization. j V+ s% X$ F! U, n
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.mp43 T. g& K2 f9 ?4 |% S& B A, i
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.pdf/ p! A2 ~2 J7 Q+ V; L- w
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.srt& U. z+ e. E, S) n
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.txt
5 q, t1 `, `: `( d; Q │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.mp47 [- X; u1 T) E! @& f
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.pdf: V2 f1 n, e/ }2 @6 U- @) X
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.srt
$ R3 w: ?. u$ e8 r/ l& X │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.txt0 e' S1 `% V& i* c# i& t( s
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.mp41 e; f( n2 p( C1 f7 X' E _% x8 ~
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.pdf
/ Z9 [4 g- u# z! p& V │ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.srt# ^0 M0 w3 ~9 k( i
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.txt
$ I) @' A1 ^& p+ ]! H! \ │ │ 04_Orthogonalization-_Computing_a_basis.mp4, [$ u6 ~- D# l$ G6 W
│ │ 04_Orthogonalization-_Computing_a_basis.pdf. E9 s! Y4 j9 J/ R
│ │ 04_Orthogonalization-_Computing_a_basis.srt$ M: D! E3 e) B) Z1 K6 D% @2 \
│ │ 04_Orthogonalization-_Computing_a_basis.txt
+ G/ y: t* D3 Q' S │ │ 05_Orthogonalization-_Orthogonal_complement.mp4
. ~) d, y5 A6 r/ K, N# d │ │ 05_Orthogonalization-_Orthogonal_complement.pdf
~+ s7 [( L$ }5 v5 O: C" L6 e │ │ 05_Orthogonalization-_Orthogonal_complement.srt' N+ K# S+ K6 ~% i4 ]% @
│ │ 05_Orthogonalization-_Orthogonal_complement.txt/ Z' K# e. c& |' B
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.mp42 \' Q4 U2 |3 [+ C' n
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.pdf
: J2 u) {/ _' ]1 m4 K │ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.srt d, d8 ?! o6 _/ c9 K4 e8 V
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.txt
- _+ c& b/ {! r" y% w/ ? │ │ 07_Orthogonalization-_The_QR_factorization.mp4
( l! X$ S% o1 ` G0 z │ │ 07_Orthogonalization-_The_QR_factorization.pdf
5 d2 P% j' a9 n& r+ ^6 E │ │ 07_Orthogonalization-_The_QR_factorization.srt( T; w. v( a/ R+ C8 T4 Q
│ │ 07_Orthogonalization-_The_QR_factorization.txt
# S4 m) l+ U, {3 M │ │ 09_Orthogonalization-_Applications_of_least_squares.mp4
# D# T3 F. b2 }4 j" U/ L) ?+ D │ │ 09_Orthogonalization-_Applications_of_least_squares.pdf, O" Y5 O( T. }0 ?. {7 F3 y- G3 P7 P
│ │ 09_Orthogonalization-_Applications_of_least_squares.srt1 S# Q, B# T" N E
│ │ 09_Orthogonalization-_Applications_of_least_squares.txt7 U% Q$ a% ^, Q. u* s
│ │ 0 d( ]3 O0 H4 w) ?
│ └─09_Tutorials4 G& a, ]' D7 Y) A' X
│ 01_How_to_submit_assignments.mp4
( ^5 ]6 T6 Y4 [& I) g; u v% p │ 01_How_to_submit_assignments.srt
/ y! R# }9 n8 q$ l E; x# W3 v │ 01_How_to_submit_assignments.txt/ Y' `; O) U* w- C0 c* O3 N
│
8 {' n# U. h, d* I6 X' T7 P ├─Big Data, Cloud Computing, & CDN Emerging Technologies# Y- d+ M6 H9 p7 Z( R8 l# J+ @5 |* L1 P
│ └─Big Data, Cloud Computing, & CDN Emerging Technologies3 x! @1 d6 Z+ ?
│ │ 下载说明.txt
; p1 w; w0 O6 E+ K# ^1 h │ │ 关注我们.png
" D) R, @4 S9 r/ |5 W8 ~4 n" | │ │ 攻城狮论坛=网络技术+编程视频.url
0 T1 L) J) n; k, f& L │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
2 |. r) k0 F8 h/ b& Y4 f, b │ │
) X. G+ @6 W6 L4 Y; |0 o+ Q │ ├─01_cloud-computing" Y k5 s* m9 Z; J
│ │ └─01_cloud-computing
, c4 c$ x% v! J │ ├─02_big-data0 g+ Z$ D V7 V
│ │ └─01_big-data
- |5 i8 u& J$ ~) ~# y │ │ 01_big-data-examples.mp48 ?/ O* v" v1 n" j; L# y
│ │ 01_big-data-examples.srt: J5 }$ w8 l+ E! M' `' k
│ │ 02_big-datas-4-vs.mp4
. d6 I! \" \0 c/ k │ │ 02_big-datas-4-vs.srt4 {$ [, d2 [1 c
│ │ 03_hadoop.mp4, n; H2 e v3 E9 U5 H( V" U% k
│ │ 03_hadoop.srt
. z: p) V0 J- V' G1 c: B │ │ 04_mapreduce-vs-rdbms.mp4
. G2 a: C3 T) {' d" ] │ │ 04_mapreduce-vs-rdbms.srt* B) ]& c; b0 M( u5 s; u1 @
│ │ 05_mapreduce.mp4
}5 L: X/ j! D4 i- c │ │ 05_mapreduce.srt, k( E' A1 W" ^3 u; Z! x: |& Y# D3 {
│ │ 06_hdfs.mp4$ Q' U: H( n+ \- w. @
│ │ 06_hdfs.srt
# X# B+ {# A6 M4 J2 f6 S │ │
4 `, A" r' \* ~ │ └─03_cdn-content-delivery-network
" E, G. w+ u; A q+ c ├─Duke Image and video processing From Mars to Hollywood with a stop at the hospital
+ d# k" p7 g- t0 X& P ├─Interactive Computer Graphics$ g/ [1 T: C. n }; M0 Y0 T
│ └─Interactive Computer Graphics& Z3 y9 B7 ^6 u% n8 Q( t
│ interactivegraphics-Torrent-Week1_All.zip.torrent! X/ u m7 P- u2 k% K$ e0 X
│ interactivegraphics-Torrent-Week2_All.zip.torrent3 R# ? O, Q/ C K; Y0 r* g0 \
│ interactivegraphics-Torrent-Week3_All.zip.torrent- Y7 g+ e% S3 Q; B) t4 F7 b' D
│ interactivegraphics-Torrent-Week4_All.zip.torrent; n8 Q6 b% L+ |% A- d4 K
│ interactivegraphics-Torrent-Week5_All.zip.torrent
- ]2 ]1 x6 A& `3 e │ interactivegraphics-Torrent-Week6_All.zip.torrent
/ f. h, e/ R/ |; E5 P │ interactivegraphics_Torrent_Week1_All.zip
& e& H6 ~8 u4 r0 T& B' A │ interactivegraphics_Torrent_Week2_All.zip! Q' L5 R3 ]4 M e
│ interactivegraphics_Torrent_Week3_All.zip; Q5 r) J+ P; o0 n+ ^7 |+ }8 l
│ interactivegraphics_Torrent_Week4_All.zip
& S7 G4 ]3 @- [: n$ O │ interactivegraphics_Torrent_Week5_All.zip8 W, m/ a& d4 k6 j6 y0 t" w
│ 下载说明.txt
3 q3 W5 C6 `4 f( U, k; T' w! m │ 关注我们.png
/ @6 K; H2 f; U+ L7 ` │ 攻城狮论坛=网络技术+编程视频.url
0 [$ F2 t. |% E3 F5 } │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
' ^) p- p6 O9 I7 u# e7 g │ ' ?6 z: T8 ]. W* z: F
├─Malik - Computer Vision7 \5 Y( t3 }- r+ W" _& V
│ └─Malik - Computer Vision4 w _' w+ G* i; F
│ 001_Overview.mp4' u W: X( `9 b5 z$ {
│ 001_Overview.srt
9 r/ ^: u. N: t5 U │ 002_Fundamentals of image formation.mp4
5 {, S4 q7 W1 c$ r; t- C │ 002_Fundamentals of image formation.srt
+ U: u+ a2 w x$ S3 [ │ 003_Fundamentals of image formation - part II.mp48 W/ \* g: K$ X+ ^4 F" L4 {1 D
│ 003_Fundamentals of image formation - part II.srt
& q: D# j9 S5 ^7 J5 E1 E0 x │ 004_Rigid body motion.mp4
d# J/ @# r7 H' k. v │ 004_Rigid body motion.srt$ d4 l8 [% W2 Q. h
│ 005_Orthogonal transformations.mp40 S# r! |0 c/ [
│ 005_Orthogonal transformations.srt
, `! b$ J: }5 t( I* r0 R │ 006_Orthogonal transformations - Orthogonal Matrices.mp4
1 \# G) G2 H+ n │ 006_Orthogonal transformations - Orthogonal Matrices.srt6 v' F* b; D3 V4 U% L
│ 007_Orthogonal matrices - Rotations and reflections.mp4
3 b* K. D9 ?8 N4 E; @/ ` │ 007_Orthogonal matrices - Rotations and reflections.srt7 d" ]# s- j: _. J: C K, O
│ 008_Parametrizing Rotations in 3D.mp4; y$ W8 T- X: s
│ 008_Parametrizing Rotations in 3D.srt
) [* C. {5 ?# Y* H' s) ^" \ │ 009_Euclidean, Affine and Projective Transformations.mp4; Y5 w( E7 f4 \" D& e" G
│ 009_Euclidean, Affine and Projective Transformations.srt
8 @, C7 D* d1 K+ R" D& ^ │ 010_Dynamic Perspective - I [15 mins].mp4, W% r g U8 u8 |! W" U0 j) b- g
│ 010_Dynamic Perspective - I [15 mins].srt
. H. k! |' F& ]" E* _4 Y, q │ 011_Dynamic Perspective - II [25 mins].mp46 V) U* X: r9 S2 F
│ 011_Dynamic Perspective - II [25 mins].srt) h3 M3 a/ D/ I5 ?8 B
│ 012_Binocular Stereo I [23 mins].mp4; B7 e9 R7 k' E4 U) Z, g5 |. Q( G
│ 012_Binocular Stereo I [23 mins].srt8 A) w/ i5 N- |, [" g$ C
│ 013_Binocular Stereo II [17 mins].mp4
7 z% m! g. P; w+ j/ |! G0 S │ 013_Binocular Stereo II [17 mins].srt+ B) c: c1 k7 o. b
│ 014_Binocular Stereo III [13 mins].mp45 j, l: l; M8 j- t( |4 c
│ 014_Binocular Stereo III [13 mins].srt
% C( g9 T" T( ] │ 015_Binocular Stereo IV - The Essential Matrix [26 min].mp4
$ l& D& r7 |* f8 I# I" ] │ 015_Binocular Stereo IV - The Essential Matrix [26 min].srt
" L* P+ X' ^9 T) e │ 016_Radiometry.mp44 \; _& v0 }. j1 R0 x. e
│ 016_Radiometry.srt# d T. t D. _' Y
│ 017_Image processing.mp46 i+ o! w. d9 ? T/ D I. y2 f
│ 017_Image processing.srt, T( s! W1 i3 L0 C* u
│ 018_Image Processing2.mp4( U* [7 z0 V+ ` ]: T
│ 018_Image Processing2.srt- ?- y2 e/ Z2 c1 H
│ 019_Orientation histograms [7 min].mp4% v4 Q9 B5 I7 v- g7 b, R! [
│ 019_Orientation histograms [7 min].srt6 Z" M9 j- u/ O8 ]5 Y
│ 020_Handwritten digit recognition - Introduction.mp41 ^/ B: K% \+ G5 {1 F7 L
│ 020_Handwritten digit recognition - Introduction.srt
7 j- `* _/ k: `8 Z │ 021_Support Vector Machines.mp45 F2 t9 j+ u& ~. [: d! l
│ 021_Support Vector Machines.srt, |5 a2 ^- I9 I) n; I
│ 022_Transformation Invariance and Histograms.mp4+ B- J5 Q8 Z& F( W+ G8 @
│ 022_Transformation Invariance and Histograms.srt4 u7 D0 q4 o! z1 q7 l
│ 023_Digit recognition using SVMs.mp4
5 e; {! c* l0 S) J m+ F, _ │ 023_Digit recognition using SVMs.srt0 `( o+ ^: L# c4 e* x
│ 024_Random forests.mp4
( H! Z/ c7 ]# v9 \ │ 024_Random forests.srt
$ `7 a4 C ~6 p; }; s# ?1 ? │ 025_Detection of 3D objects.mp4
# H# @1 i: { P# j8 U* Q: w, h │ 025_Detection of 3D objects.srt, U; v: P+ w c! z) H
│ 026_Concluding Remarks.mp4* K2 a' Y, ~# W2 G- d) n" J
│ 026_Concluding Remarks.srt. Q' {0 S* a; O/ i/ R
│ 下载说明.txt2 v: X* \0 p5 h# c. V# D+ \
│ 关注我们.png
7 Y, J Y; O" h: { │ 攻城狮论坛=网络技术+编程视频.url
2 c9 D- k% I! V+ I3 I │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt* E4 N- O/ v* c# P
│ & ~6 V% B; A$ a
├─Practical Machine Learning
. @! ~% r( K$ O# X* b( d# v# v5 x8 M │ └─Practical Machine Learning; I4 C5 i' x7 s9 N) o7 X
│ │ 下载说明.txt
( N' b$ H1 w! g D$ I │ │ 关注我们.png
5 e. X( h1 L: y2 c k* N │ │ 攻城狮论坛=网络技术+编程视频.url# u% k# o2 h9 F! g' I9 H4 U4 R1 K& U m
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt4 z0 o! v, p. k" {3 M8 s
│ │
4 j6 N6 u+ J. ~$ J │ ├─Week1! s5 I+ Q1 f/ E: ? J4 j
│ │ 001predictionMotivation.pdf a5 e8 P5 u0 d# o3 i: |' z3 v
│ │ 002whatIsPrediction.pdf5 l# M( [$ Y/ |. R, O9 ]3 |/ D& S4 T
│ │ 003relativeImportance.pdf. i# T* O4 y9 x6 A; J) a- L
│ │ 004inOutSampleErrors.pdf
; M' _& ?+ q# z ] E3 T │ │ 005predictionStudyDesign.pdf
+ a/ c8 z2 M4 O9 F9 A" D │ │ 006typesOfErrors.pdf5 R: t+ j% C0 K4 n
│ │ 007receiverOperatingCharacteristic.pdf
2 d+ F. l0 f. o: ?5 N │ │ 008crossValidation.pdf, y9 z2 E) ^1 ^) Z* i
│ │ 009whatData.pdf, s7 \8 T, o6 _" ?7 \ ^6 s: h3 o
│ │ 1 - 1 - Prediction motivation (8-26).mp4$ T( z) H- b3 d! {; ]5 Q! ^
│ │ 1 - 1 - Prediction motivation (8-26).srt0 P9 o0 ^; W# E' J1 A
│ │ 1 - 2 - What is prediction- (8-39).mp4& K! R' f+ Z. s) }
│ │ 1 - 2 - What is prediction- (8-39).srt: i) T0 y5 S4 K/ B a8 g
│ │ 1 - 3 - Relative importance of steps (9-45).mp4( f8 H8 Z# E7 _
│ │ 1 - 3 - Relative importance of steps (9-45).srt
( l4 j/ O3 A, |: c1 v; c+ E" i │ │ 1 - 4 - In and out of sample errors (6-57).mp4! x: S' V7 \4 _, D
│ │ 1 - 4 - In and out of sample errors (6-57).srt
3 b/ g# R& g3 B1 z5 D3 i │ │ 1 - 5 - Prediction study design (9-05).mp4
5 T" Q# R4 _- B, T │ │ 1 - 5 - Prediction study design (9-05).srt
8 ]" t9 y4 Y1 U1 _3 _3 R+ ~6 t │ │ 1 - 6 - Getting Data Overview (1-34).mp4
6 m; c8 E# h) _: ?( e& n0 ?6 O │ │ 1 - 6 - Types of errors (10-35).mp48 b5 c/ `0 w* r8 G3 B1 ~9 f8 R6 _8 B
│ │ 1 - 6 - Types of errors (10-35).srt
- Y$ |) J3 j c& d# i b0 W │ │ 1 - 7 - Receiver Operating Characteristic (5-03).mp4# I1 |/ l3 [2 b6 s l
│ │ 1 - 7 - Receiver Operating Characteristic (5-03).srt$ b& U# S, N! g, D; N, _/ M/ E
│ │ 1 - 8 - Cross validation (8-20).mp4! ]; {* D$ }9 _9 J2 P% ~) `; W2 N
│ │ 1 - 8 - Cross validation (8-20).srt9 x6 T7 Z5 A: o e, D
│ │ 1 - 9 - What data should you use- (6-01).mp4, Q3 Q: H6 d& Y" T) t, U$ `
│ │ 1 - 9 - What data should you use- (6-01).srt% g; M! @5 P. a3 a. I( T, o
│ │ ; Z; I2 ^% |3 q& A3 |
│ ├─Week2% |+ }1 d5 ~ ^" ]. v
│ │ 010caretPackage.pdf/ ~8 z K! M! ]( M
│ │ 011dataSlicing.pdf
! s+ B3 d' z* l5 b6 D1 _; W& Y │ │ 012trainOptions.pdf1 Z+ i2 V# y- F) d$ ]; b# K
│ │ 013plottingPredictors.pdf% U* V0 N. W6 S; l8 l- K
│ │ 014basicPreprocessing.pdf( Z; o/ x) P2 l9 J& C# J7 @) X( B
│ │ 015covariateCreation.pdf- l; F9 w/ n" K, o) x1 f
│ │ 016preProcessingPCA.pdf
. S5 g% Y) y" {% ?, `3 N: H+ I │ │ 017predictingWithRegression.pdf0 {) ?# ?- W( n1 d* T3 ]: r# v) Y
│ │ 018predictingWithRegressionMC.pdf+ k9 t9 @6 U; G* z
│ │ 2 - 1 - Caret package (6-16).mp4
+ F9 U3 P1 h- D9 a; w │ │ 2 - 1 - Caret package (6-16).srt$ t+ q4 g0 `9 i2 u( ~6 z4 k1 v0 L
│ │ 2 - 1 - Caret package (6-16).txt) k9 q. q/ p: o! h& `! w: Q
│ │ 2 - 2 - Data slicing (5-40).mp4) a: U* a# t Z2 O
│ │ 2 - 2 - Data slicing (5-40).srt
" ~7 N5 q1 Z- Y( u/ } │ │ 2 - 2 - Data slicing (5-40).txt
* L' V3 v3 R- e) J2 }) A% o │ │ 2 - 3 - Training options (7-15).mp4 `$ L+ F* b7 c, d8 k6 R
│ │ 2 - 3 - Training options (7-15).srt3 f. v, g y2 B z( P) Q+ d, L! z
│ │ 2 - 3 - Training options (7-15).txt9 h7 ?6 T1 p# M% h8 S: u
│ │ 2 - 4 - Plotting predictors (10-39).mp4" k, f O: P3 ~4 e- r
│ │ 2 - 4 - Plotting predictors (10-39).srt
8 u, O r5 \% p( x1 b7 ^ │ │ 2 - 4 - Plotting predictors (10-39).txt g3 z% ^$ S1 s( g+ U9 D. @5 K
│ │ 2 - 5 - Basic preprocessing (10-52).mp4$ Y: M* u3 M0 I' l! \
│ │ 2 - 5 - Basic preprocessing (10-52).srt
( G! `) K V+ B8 F │ │ 2 - 5 - Basic preprocessing (10-52).txt; d- j1 N$ [* E/ a& N, A
│ │ 2 - 6 - Covariate creation (17-31).mp4
( ]9 w: @! q0 O3 C" g | │ │ 2 - 6 - Covariate creation (17-31).srt" l8 n/ w: K; r3 ]
│ │ 2 - 6 - Covariate creation (17-31).txt
/ J. V' H* r$ I. c8 G2 ?/ e% ^8 G- ` │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).mp4
0 m- }3 g* j* Z; [2 `# ?& F │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).srt
" k0 `1 A: p8 o: G │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).txt" R( A+ j) y. i1 f* J
│ │ 2 - 8 - Predicting with Regression (12-22).mp44 l" j! m5 s" V# v8 _, q
│ │ 2 - 8 - Predicting with Regression (12-22).srt
3 m% u/ {$ [7 ^8 \. W3 k │ │ 2 - 8 - Predicting with Regression (12-22).txt
1 m8 a8 c Q* E │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).mp4# x9 B0 w1 b4 ~" g
│ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).srt
, p/ P7 H `5 H, m │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).txt9 D3 m/ S, z/ E- p/ r" ]5 F
│ │ ! I5 x0 q: ?3 T, g0 \ P0 E4 G
│ ├─Week34 S# z T# E8 l( ^" D
│ │ 019predictingWithTrees.pdf$ l* j" ?2 [# W( V) s+ O' Q
│ │ 020bagging.pdf, a5 D8 S f9 K% E' U8 \
│ │ 021randomForests.pdf
1 A- x/ K6 M5 W' j" [, t │ │ 022boosting.pdf4 a, P3 C y9 A
│ │ 023modelBasedPrediction.pdf7 Q4 u+ f$ H, Q
│ │ 3 - 1 - Predicting with trees (12-51).mp43 w8 t! @' G) U/ R/ ]; I ~. a& u
│ │ 3 - 1 - Predicting with trees (12-51).srt
7 [% U* u9 d9 X9 n+ x: r │ │ 3 - 1 - Predicting with trees (12-51).txt. G$ C# z) G4 z- Y/ G! c. v
│ │ 3 - 2 - Bagging (9-13).mp4
' D& ~( f( [2 {: A' o) E │ │ 3 - 2 - Bagging (9-13).srt
, U+ A( q% H2 Z4 y+ x │ │ 3 - 2 - Bagging (9-13).txt
% K3 i$ t- K' i6 t' G% O' x4 N │ │ 3 - 3 - Random Forests (6-49).mp4
! n" E* r; L- L& \$ ] │ │ 3 - 3 - Random Forests (6-49).srt1 r3 `: b1 j1 o4 Q. E8 w
│ │ 3 - 3 - Random Forests (6-49).txt, e9 t) k/ U' p( h' t r
│ │ 3 - 4 - Boosting (7-08).mp46 i, l4 H B9 A. y9 L
│ │ 3 - 4 - Boosting (7-08).srt
* x' M# X8 k: U2 S% ? │ │ 3 - 4 - Boosting (7-08).txt. H5 d; U3 l2 [) v7 X
│ │ 3 - 5 - Model Based Prediction (11-39).mp4) }. Y$ x6 R$ q
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│ │ 3 - 5 - Model Based Prediction (11-39).txt
! n1 Q/ e* Q" n% V& `, A+ y │ │ . V* ]$ @; m1 ~
│ └─Week4
% R" V1 T/ U, `: u, ~7 k4 U* n q │ 024regularizedRegression.pdf6 O9 b6 s8 G9 B/ q
│ 025combiningPredictors.pdf0 ~ {; w8 _+ ]$ g6 p
│ 026unsupervisedPrediction.pdf
0 p" W- O2 U. F* V0 A/ Y; g% X3 t │ 027forecasting.pdf8 @) w4 v* {+ y6 C& j* X
│ 4 - 1 - Regularized regression (13-20).mp4: }) }- q! E. F) ~; c( ]
│ 4 - 1 - Regularized regression (13-20).srt; d5 g K5 s# j1 U1 U- N
│ 4 - 1 - Regularized regression (13-20).txt' H0 T5 }! b2 w/ e
│ 4 - 2 - Combining predictors (7-11).mp4
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│ 4 - 2 - Combining predictors (7-11).txt
) p8 A6 `( ], @5 ] j6 c4 p/ g │ 4 - 3 - Forecasting.mp4/ {, i7 j* Y% Y! [9 R" I; t8 z6 P
│ 4 - 3 - Forecasting.srt
9 A7 d2 J* g3 d- I │ 4 - 3 - Forecasting.txt
+ c) o: i7 b8 i# g b │ 4 - 4 - Unsupervised Prediction (4-24).mp4/ {% |( s+ G, E B( t
│ 4 - 4 - Unsupervised Prediction (4-24).srt
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6 Y3 L! R: H, b& H; O! f* c0 } ├─Sapiro - Image and video processing d6 {0 r) `1 z$ E2 n2 s
│ 01.pdf
1 \, `) S+ S7 r/ t: p4 d2 v │ 02.pdf$ g- e, m& x8 O, G6 c
│ 03.pdf
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│ 06.pdf
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│ 08.pdf
. T* D" t( h( A3 {+ G │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.mp4
0 i M# T; V" o$ B │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.srt
4 N. P/ H. x5 T/ O; j │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.txt
" S; b' u) r& { │ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.mp4
7 i7 a5 [" N' ~& X1 }4 x( p3 C │ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.srt1 N6 \& @& n; e% p+ [! i+ x6 R3 K/ O
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.txt4 Y' J) u5 P2 ` i; ~$ z: q
│ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.mp4
! Z' h9 y' d+ j2 W+ S; s │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.srt
6 P0 M8 S1 Y& J3 ~: Y │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.txt! z: Z/ i4 |9 h: \( K
│ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.mp4" V9 G; p6 y% D% h# }+ q
│ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.srt
6 U2 J- _8 ^, x7 ^: h0 {' {3 @3 o │ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.txt
) i3 p6 ]0 p; H │ 10 - 5 - 4 - Thanks.mp4$ j8 K$ U) b7 [& F( l
│ 10 - 5 - 4 - Thanks.srt
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│ 2 - 1 - 0 - Welcome and Start Here.mp42 v3 Z/ u4 m1 a) l
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d' \% K$ l, d& z │ 2 - 1 - 0 - Welcome and Start Here.txt
" H* V- {" A" ~4 j │ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.mp4: m7 w* b% S* |' ?/ |
│ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.srt
5 @) u# ^6 ^" ~& ]' v* U" x │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.mp41 N4 }) H; t0 K
│ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.srt4 X! q5 Z2 J5 d* z2 {
│ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.txt, ]! n, B. E$ r" [
│ 2 - 4 - 2 - Course logistics - Duration 0242.mp4
' S% t7 i, J( |- g' K; k0 d' x │ 2 - 4 - 2 - Course logistics - Duration 0242.srt* E E" L* L; g6 ~7 O& ]; R: U
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│ 2 - 5 - 3 - Images are everywhere - Duration 0631.mp40 A! o$ T" q) h' e/ b# d
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.srt
- x; X, P! Y* k0 O7 _4 `0 s │ 2 - 5 - 3 - Images are everywhere - Duration 0631.txt
; m; j. M# G$ g* f │ 2 - 6 - 4 - Human visual system - Duration 1710.mp4$ x& K! c7 W H
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│ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.mp4% Y6 ^4 w% }/ r
│ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.srt
" l* j3 e: C' f, [ │ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.txt& r$ [- L) s# \# |) {& V9 Q. D
│ 2 - 8 - 6 - Simple image operations - Duration 1705.mp4
) `0 w2 X z' A* J │ 2 - 8 - 6 - Simple image operations - Duration 1705.srt$ F4 Q. C" v) Q% C' k0 u# A% e
│ 2 - 8 - 6 - Simple image operations - Duration 1705.txt
! r8 u k* J% y, o& x, M+ e │ 3 - 1 - 1 - The why and how of compression - Duration 1416.mp4
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│ 3 - 1 - 1 - The why and how of compression - Duration 1416.txt
; z8 r- W1 g2 i; N% g5 L( I │ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.mp4
$ L* b8 |0 D5 ~- s% l" ]! v5 A │ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.srt
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: `+ \; o5 P4 P# \1 ?6 H4 U │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.mp4
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│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.mp4+ I' f) C: }# x% d( v1 h E0 s# Z
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.srt3 Z" a+ k" l1 [5 V8 l
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.txt; p( _! o! C0 B' F0 |1 T' Z
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.mp4 }; B& Z' y! S; O
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.srt
$ X) m) p I4 @ │ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.txt
2 n( \( o$ C2 g8 d0 k/ }. E │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.mp4
( A& x7 t: G, H │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.srt
" b' \* D: C, c* q. d; Y │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.txt
5 G% A9 `, M: c6 p2 y3 D │ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.mp47 N) Y1 I4 q9 B9 Q
│ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.srt" O! @7 x2 H4 R3 V: N$ D
│ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.txt# f& w, P' r) a
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.mp4
; H ~ k/ [ n$ N │ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.srt C5 A- A$ L% V$ d
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.txt
, X3 ?* x# L/ ]- M Q: H. T& R │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.mp4
' x- a/ @7 L2 M5 x" \ x │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.srt
" [% \ S: `3 ~& J │ 4 - 10 - 10 - Demo - Median filter - Duration 0131.txt: P" ?6 {. v+ t4 @0 |
│ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.mp4
! O1 [" w& t! }! z │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.srt2 N$ e0 \9 ?8 [
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│ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.mp4
; T+ e9 s( k$ g: u& V' {! E │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.srt L* i5 E0 i/ v" ?3 h! j
│ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.txt
) @1 t4 @! _$ h │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.mp49 _/ j* y7 O% t: R
│ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.srt9 ^# G9 @' t) c) @$ w
│ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.txt
+ p3 Y v* e6 D. e │ 4 - 14 - 14 - Concluding remarks - Duration 0112.mp4
2 q* _7 f1 A! _ │ 4 - 14 - 14 - Concluding remarks - Duration 0112.srt
1 I5 S! Z2 U! z │ 4 - 14 - 14 - Concluding remarks - Duration 0112.txt
/ w/ ~. @$ d0 ^ S3 G5 v' X) |9 ` │ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.mp4) {- J9 I$ p4 J% \) O0 t
│ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.srt% M" W+ W& Z7 v5 n/ w
│ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.txt/ v% r L$ k2 @$ v) ?0 y
│ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.mp44 b9 }9 B* Y. S% N$ `4 ]
│ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.srt% m7 z9 v/ Y! P
│ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.txt
* A; V9 s: q. p' m7 } │ 4 - 4 - 4 - Histogram matching - Duration 0831.mp4$ \( k7 U \9 Q1 R+ B( M+ m6 O
│ 4 - 4 - 4 - Histogram matching - Duration 0831.srt
& p. D3 J& {$ w! x3 ? │ 4 - 4 - 4 - Histogram matching - Duration 0831.txt
4 s+ H0 Q& V* p K" t3 d! o4 Q │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.mp4
) X: X! ]7 k; Z3 E8 O }( B2 I │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.srt/ J/ O# Z% ?1 p3 h
│ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.txt$ K0 J" K( I" Z( o: z8 B/ O
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.mp4
! c* U0 T5 |3 u. e# b* e6 _( |+ k │ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.srt
. S' k1 K* @( ?1 { │ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.txt, {/ p( x4 B9 Y* j
│ 4 - 7 - 7 - Non-Local means - Duration 0727.mp4
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│ 4 - 7 - 7 - Non-Local means - Duration 0727.txt( w$ N, P/ N: h
│ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.mp4
" m4 t# z, q! h, ^& O. [9 k │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.srt
# v. T1 c% P4 z+ Q. `1 s7 D8 \ │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.txt( _$ i+ t- h B1 {: d8 }
│ 4 - 9 - 9 - Median filter - Duration 0720.mp4
! G: t" q4 l9 w5 g' s │ 4 - 9 - 9 - Median filter - Duration 0720.srt
1 b1 M% [; u6 {" R P A0 d │ 4 - 9 - 9 - Median filter - Duration 0720.txt
0 v5 O: z/ r+ ?7 h) T9 o │ 5 - 1 - 1 - What is image restoration - Duration 0749.mp4
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│ 5 - 1 - 1 - What is image restoration - Duration 0749.txt
" \9 i# d9 |# \2 K3 f │ 5 - 2 - 2 - Noise types - Duration 1243.mp4
5 K9 N2 p! }& F5 l: ^# j# a │ 5 - 2 - 2 - Noise types - Duration 1243.srt% o( t# p6 S; A; U! d* |# W
│ 5 - 2 - 2 - Noise types - Duration 1243.txt
0 }. q# c7 H, K$ X' F1 V2 ]% v" `+ H │ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.mp4
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│ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.txt
4 G' Q% L" {' Y7 N+ }7 L │ 5 - 4 - 4 - Noise and histograms - Duration 0452.mp4
8 {9 Q, p4 K% A/ V% x) k' q( s; x │ 5 - 4 - 4 - Noise and histograms - Duration 0452.srt/ W6 Y W$ ^# V. l$ k0 b2 B
│ 5 - 4 - 4 - Noise and histograms - Duration 0452.txt7 v* K: z* z2 E. X
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.mp4
+ u& T. I! g2 [5 V' Z │ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.srt0 ] T9 r$ H8 q+ b- _6 M
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.txt
/ I( u/ ?# v9 B# R1 |" r. E' ` │ 5 - 6 - 6 - Degradation Function - Duration 1140.mp4
# ^* q" t+ c6 S) `& z& F │ 5 - 6 - 6 - Degradation Function - Duration 1140.srt; Y& F$ q8 n! J2 t3 ~
│ 5 - 6 - 6 - Degradation Function - Duration 1140.txt
) ]" D# k" ^% u6 [ │ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.mp44 `( e% G( C3 D9 B+ i& H/ D
│ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.srt
' r x: \1 P0 _ │ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.txt
2 U0 x4 L: y' c1 h │ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.mp4! c$ R \$ R6 ^6 S/ b( x
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.srt
7 y' P3 G8 }* i& u │ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.txt
; L' G/ D& h) b) z, O6 A │ 5 - 9 - 9 - Concluding remarks - Duration 0033.mp4. M, V3 L c" j9 u% n
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│ 5 - 9 - 9 - Concluding remarks - Duration 0033.txt
9 u7 g0 B* I& |* k; f: ?- O! q7 U │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.mp4
w- t4 V# |2 f1 p4 F4 P, A │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.srt
0 l3 o- |" ]. i ?4 O │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.txt
) u5 E# x$ Z. A$ [2 R2 o │ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.mp4) h+ T- S3 Y3 l6 b4 y' N) Q; Y
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.srt
8 ?9 H4 X9 ]# y │ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.txt
) |& r2 e ^4 I! s) Y8 X% x4 p9 } │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.mp4
1 g+ |- S% M9 y k │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.srt: ], E$ X2 \( r
│ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.txt
- Z* N2 Y, A6 T& p │ 6 - 12 - 12 - End of the Week - Duration 0021.mp4* [% b2 Q: q! @) Q* L# R
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% H( C; I7 d. I5 K( r( e │ 6 - 12 - 12 - End of the Week - Duration 0021.txt
8 q/ L, _" f6 @" y+ U6 }# v% g │ 6 - 2 - 2 - On Edges and Regions - Duration 0517.mp4
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│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.txt
) v( e' I2 e% O; v │ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.mp4
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│ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.txt
# l1 N9 O& ~$ U2 m! ]7 Y │ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.mp4
9 ^' W$ _( O* o7 _" L7 \ │ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.srt# O& U5 I( l! l8 Q2 ]
│ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.txt
6 ~- Z8 t. y$ ?$ E5 G0 N │ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.mp4% ^: ] ~( l3 o
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│ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.txt; c/ T, q9 X# w0 U. h. R
│ 6 - 6 - 6 - Congratulations - Duration 0017.mp4
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7 C& |1 Q: P0 ?$ Y" d' T │ 6 - 6 - 6 - Congratulations - Duration 0017.txt. g) p. Q1 c6 {& H" P. \
│ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.mp4$ X8 }5 |9 [1 ]
│ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.srt
0 V2 U8 [5 e( ]4 c │ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.txt# k- y6 G2 l0 q$ B" V: R5 O- R
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.mp4
& E/ R! @/ e/ s │ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.srt" p: ?9 S ~- _& W2 ^
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│ 6 - 9 - 9 - Mumford-Shah - Duration 0550.mp40 y* ] f4 d# l
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! R/ d }# `" w, l! a: J$ F5 g │ 6 - 9 - 9 - Mumford-Shah - Duration 0550.txt
( C) v' j, o# ?, D │ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.mp4 C3 m2 ^3 X& f7 G& H. Y1 e, b
│ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.srt& b& }' M. k2 O
│ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.txt
+ t g0 r+ ]4 V& c( Q; u# |4 n/ Q │ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.mp4
( q; E* S% ]4 S0 Z │ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.srt
+ u3 \7 X( j3 t2 O3 U Z │ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.txt+ o6 X* n1 H" s6 K9 o4 ^8 F9 @3 ]
│ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.mp4
7 b6 Y* [, |$ v" [0 ?- n8 b+ Q! N │ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.srt
" ?; C$ r# ^% ^( u) |' o: O │ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.txt" x* d4 b) W- t' C$ L% |
│ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.mp4$ L6 s- Z/ d$ I* E7 Z# q
│ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.srt
5 t9 P9 t& |+ [ |# ?. s$ z7 [ │ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.txt
& R" B# R; s. T' q4 U │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.mp4: R) C! ~8 h* @; X* u
│ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.srt
4 _! G) t& n7 \2 y( K; t; e │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.txt
+ v9 I4 V! a# S3 ] m! s7 i │ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.mp4
" [& \3 d9 l& o0 g- Y: k9 \ │ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.srt
4 `! }, S, ~, [ J5 } │ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.txt
; o3 T6 K5 K6 h, ^7 i/ n │ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.mp4
% Y( c: a, W" N. [ │ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.srt
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2 ~# t' @' r8 Y$ s" E' b$ b │ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.mp4
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│ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.mp4
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# ?5 Z( I* K; X) k │ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.txt
! L! A6 y8 p! N/ C5 {! S │ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.mp47 x( T5 S- q$ r
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+ S! M. v9 ^- G, o │ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.mp47 {# K& a B! D V' n, i# [
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$ h4 s) j* Y! \8 {2 ~ │ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.mp4
* _6 J- ~, C8 ~" ? │ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.srt
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" W/ b8 V* h1 r0 \6 t; \ o │ 8 - 5 - 5 - Smart Cut and Paste - Duration 0751.mp4
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│ 8 - 6 - 6 - Demo - Photoshop Inpainting Healing Brush - Duration 0231.mp42 S: g8 \7 ]& o
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1 g/ j- F5 F. i3 y │ 8 - 7 - 7 - Video Inpainting and Conclusions - Duration 0513.mp4& u, R" K' E9 `: j. U7 d1 ^+ Y3 T
│ 8 - 7 - 7 - Video Inpainting and Conclusions - Duration 0513.srt
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│ 9 - 1 - 1 - Introduction to Sparse Modeling - Part 1 - Duration 1039 .mp4
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