: P" y( S7 v' G+ R ├─001_machine_learning_by_andrew_ng_coursera0 J- Q, [$ H) C
│ └─001_machine_learning_by_andrew_ng_coursera7 f( Q% a1 s+ E" s
│ ├─ml-003: e1 J- q* h$ R: \ H+ R, J
│ │ ├─01_I._Introduction
& A3 [8 t% Y3 w7 m( d3 \7 X7 Y │ │ │ 01_Welcome.mp4" ]$ X T8 R+ m
│ │ │ 01_Welcome.pdf
# m" `7 s: B( Y) G+ ~; l │ │ │ 01_Welcome.pptx! J$ Y# n6 U* P, O6 {' Z
│ │ │ 01_Welcome.srt& o/ {4 s, u/ k# c
│ │ │ 01_Welcome.txt1 w* T% `# U9 `/ U6 |5 _4 m
│ │ │ 02_What_is_Machine_Learning.mp4, V6 v$ j+ g# R5 X" P: I% M
│ │ │ 02_What_is_Machine_Learning.srt6 G9 _0 T3 H0 j) {6 S, r4 p* G
│ │ │ 02_What_is_Machine_Learning.txt1 I( v" y8 T) s# X9 J
│ │ │ 03_Supervised_Learning.mp4
4 |5 B1 P2 d- e: a" ^% o6 ^( N │ │ │ 03_Supervised_Learning.srt
! F# r8 Q! s. T' ]4 ? │ │ │ 03_Supervised_Learning.txt) G6 A/ a5 ^! T$ E1 O
│ │ │ 04_Unsupervised_Learning.mp45 O0 D/ c- s* Z) C
│ │ │ 04_Unsupervised_Learning.srt
4 C5 L4 v, T# K& I8 B# L │ │ │ 04_Unsupervised_Learning.txt
) y$ E- X5 i; d% R │ │ │ ! i. ]2 a9 d7 d' q% @
│ │ ├─02_II._Linear_Regression_with_One_Variable' @% l& e4 \, s% N
│ │ │ 02_Cost_Function.mp4
; g3 \+ s5 _9 X% j! Y' e1 i │ │ │ 02_Cost_Function.srt
+ L9 u* ^2 B% B. m1 J3 @% H │ │ │ 02_Cost_Function.txt! O7 `' ~% k6 |# o1 j7 p: U
│ │ │ 05_Gradient_Descent.mp4+ c, ^- D% o* x# W
│ │ │ 05_Gradient_Descent.srt/ X; S* u$ O2 }$ i$ m- {+ d
│ │ │ 05_Gradient_Descent.txt
% X+ D8 ?% t) ?! C) e' ^. G │ │ │ 08_Whats_Next.mp47 ~- k, G* y- O
│ │ │ 08_Whats_Next.srt
7 |; m0 M' q S. W5 C │ │ │ 08_Whats_Next.txt$ c- g. M$ h0 c1 {5 j/ m
│ │ │ 2 n' O1 m7 y- o
│ │ ├─03_III._Linear_Algebra_Review
' B/ ?0 Q3 X6 ]/ w0 N+ R3 Q$ U │ │ │ 01_Matrices_and_Vectors.mp4
5 G; L# V. c4 F; D$ A2 N │ │ │ 01_Matrices_and_Vectors.pdf
& z; l' f" ^$ w, A │ │ │ 01_Matrices_and_Vectors.pptx& D" m. i$ `1 z" f* K
│ │ │ 01_Matrices_and_Vectors.srt
$ Q: p, L/ k& u$ ^$ ]8 `# O9 X │ │ │ 01_Matrices_and_Vectors.txt
* K0 ]* g `$ p( R, m │ │ │ 03_Matrix_Vector_Multiplication.mp4$ h" x: G+ y, \" J
│ │ │ 03_Matrix_Vector_Multiplication.srt* c) L' N7 w3 A3 ]
│ │ │ 03_Matrix_Vector_Multiplication.txt f4 K6 {" r$ \1 ?
│ │ │ 04_Matrix_Matrix_Multiplication.mp41 ?' u- Q2 S5 c4 t- r
│ │ │ 04_Matrix_Matrix_Multiplication.srt. j1 T# j, h$ A* c7 b, U4 D. W) @
│ │ │ 04_Matrix_Matrix_Multiplication.txt
/ g, m, N9 |0 ?7 _' o │ │ │ 05_Matrix_Multiplication_Properties.mp4
6 e, }# Q2 V9 }1 g" x; y/ M │ │ │ 05_Matrix_Multiplication_Properties.srt" N1 q. M: P5 B) d
│ │ │ 05_Matrix_Multiplication_Properties.txt
: A* y P5 ` S/ b4 c1 Y# G( _ │ │ │ 06_Inverse_and_Transpose.mp4
/ X& H# y- d: L5 E; i' p! M │ │ │ 06_Inverse_and_Transpose.srt
5 @- a! X& W# w │ │ │ 06_Inverse_and_Transpose.txt2 i5 W% N) d1 S2 Q* k
│ │ │ 3 U6 _& l/ T' M& D$ G# F
│ │ ├─04_IV._Linear_Regression_with_Multiple_Variables+ h- s( | p2 ~/ B7 p
│ │ ├─05_V._Octave_Tutorial/ G4 E2 S4 \3 d$ S: R
│ │ │ 01_Basic_Operations.mp4
" s1 s+ k+ Y$ c3 O* a# x │ │ │ 01_Basic_Operations.pdf
, s% E/ @" T$ V' m. ]/ ? │ │ │ 01_Basic_Operations.pptx
( L; x& }- u2 |1 o1 C& h; q; Q │ │ │ 01_Basic_Operations.srt# a' W0 f$ N0 C" P; U
│ │ │ 01_Basic_Operations.txt
. B. O7 w) C& C. E │ │ │ 02_Moving_Data_Around.mp4: k3 V, K2 E: n8 [2 @0 K( t/ P5 U+ e
│ │ │ 02_Moving_Data_Around.srt' J) Q( r) O& _1 P( H# R5 Y
│ │ │ 02_Moving_Data_Around.txt) |7 D9 `5 R3 \$ L) x+ U5 L! M
│ │ │ 03_Computing_on_Data.mp4
) X, i1 B& N8 `9 H │ │ │ 03_Computing_on_Data.srt0 w7 H4 I4 m. |) x# X) h$ ~- H- M
│ │ │ 03_Computing_on_Data.txt$ n8 i: u0 j/ S9 G
│ │ │ 04_Plotting_Data.mp4
" n5 X& U( V# ]6 t3 h* B0 k& g1 b+ R9 d │ │ │ 04_Plotting_Data.srt
1 e* w. Q9 R7 U2 |0 G! r' S │ │ │ 04_Plotting_Data.txt8 |+ i) `7 r) l% `. u8 O9 D1 |3 y
│ │ │ 06_Vectorization.mp4% E9 C% e3 ?/ q3 L
│ │ │ 06_Vectorization.srt
* u3 V( S8 j/ Q7 W2 J" c │ │ │ 06_Vectorization.txt# [3 u% V% n6 _0 e* J8 w) o% h& I
│ │ │
; n8 d/ ~( [8 D3 A, B │ │ ├─06_VI._Logistic_Regression
/ h* H) f+ y1 N& o │ │ │ 01_Classification.mp4
, J8 M/ @, c( v0 m. f% l; y- O8 s │ │ │ 01_Classification.pdf
. I2 c9 V: k. [% R) S │ │ │ 01_Classification.pptx9 u( t- I0 r4 h8 z6 p8 s4 v
│ │ │ 01_Classification.srt2 w- t0 E9 V' f' h
│ │ │ 01_Classification.txt. e. x8 L4 I3 S0 |) E2 S/ m
│ │ │ 02_Hypothesis_Representation.mp4- C w2 w4 ^/ \' }, p! C
│ │ │ 02_Hypothesis_Representation.srt
0 [' g1 [) r- ` │ │ │ 02_Hypothesis_Representation.txt
1 x% G3 T( |* O1 C │ │ │ 03_Decision_Boundary.mp48 D9 t6 U- M& B$ Y- ]
│ │ │ 03_Decision_Boundary.srt
" E$ ?! m; N9 ~7 E8 |1 O* G │ │ │ 03_Decision_Boundary.txt
! f7 W0 n" h6 S- ] │ │ │ 04_Cost_Function.mp4) P0 y5 ~0 q& k; a, V
│ │ │ 04_Cost_Function.srt% o' h' Q# x: z+ M2 e. P5 o
│ │ │ 04_Cost_Function.txt
- H1 `7 `8 P! N/ L │ │ │ 06_Advanced_Optimization.mp4
1 A% u% p1 k6 d/ D, w3 u& U │ │ │ 06_Advanced_Optimization.srt
( S& C% b" V P. ?; l" Y │ │ │ 06_Advanced_Optimization.txt
0 U5 o; S" u2 Y5 r4 {; m1 _ │ │ │
% q7 v* @5 E( p6 h c6 k6 ? │ │ ├─07_VII._Regularization
/ u& }9 k3 T3 ]9 C$ l* O │ │ │ 01_The_Problem_of_Overfitting.mp4
* Z+ n$ j9 ]( N" g6 d │ │ │ 01_The_Problem_of_Overfitting.pdf1 M% w1 F) y5 p+ h+ H7 x* N
│ │ │ 01_The_Problem_of_Overfitting.pptx+ c9 j2 R! k' [: U( t
│ │ │ 01_The_Problem_of_Overfitting.srt# |% J$ N4 G' b/ J' b$ j; L
│ │ │ 01_The_Problem_of_Overfitting.txt
6 ~) r# C G! B% [( @ │ │ │ 02_Cost_Function.mp4
3 g. q/ K3 d" h( ^ │ │ │ 02_Cost_Function.srt2 f: k0 l/ Z. _- U8 L% X
│ │ │ 02_Cost_Function.txt1 Q( A* p% E# D% Q
│ │ │ 03_Regularized_Linear_Regression.mp4" O$ a) X7 ~5 f! m
│ │ │ 03_Regularized_Linear_Regression.srt2 [0 q: Q) M* s+ B I8 u
│ │ │ 03_Regularized_Linear_Regression.txt( B" d. z* Z% U6 c: g
│ │ │ 04_Regularized_Logistic_Regression.mp4+ a" @( S. d, c9 C+ T# V' p
│ │ │ 04_Regularized_Logistic_Regression.srt6 j' T0 _( P. J* ]6 Q2 P; u; J
│ │ │ 04_Regularized_Logistic_Regression.txt2 Z1 f; a) E2 g) q! W* F- ^
│ │ │ 0 h( B$ x: F- v+ O, m( c0 }3 u* U
│ │ ├─08_VIII._Neural_Networks-_Representation
( @3 q2 ~+ W3 @. ~ |2 j │ │ │ 01_Non-linear_Hypotheses.mp4
7 [% Y4 x; D2 J7 z$ x$ v │ │ │ 01_Non-linear_Hypotheses.pdf8 d4 c5 C6 U, V: Z; a) l
│ │ │ 01_Non-linear_Hypotheses.srt" L1 D# X' K0 w8 R8 K9 Z4 d
│ │ │ 01_Non-linear_Hypotheses.txt
+ @! J; W3 Z" V$ _% L& c' [6 z │ │ │ 02_Neurons_and_the_Brain.mp4
" x' n( M7 V0 h2 {) G9 ?' U9 G │ │ │ 02_Neurons_and_the_Brain.srt
; p8 y8 K `, U6 P' t! ] │ │ │ 02_Neurons_and_the_Brain.txt" U; [1 V, ~/ E! ?) L
│ │ │ & `+ L1 Q7 u4 l3 M* U$ i
│ │ ├─09_IX._Neural_Networks-_Learning+ _2 O, L/ h' |3 [( l
│ │ │ 01_Cost_Function.mp4
7 N) e0 ?+ x: y% I │ │ │ 01_Cost_Function.pdf# F/ K$ i1 c( `6 K6 t! T! w
│ │ │ 01_Cost_Function.pptx
1 O* G: u$ R# `% ` │ │ │ 01_Cost_Function.srt
) n" j5 @" |" _% z+ e1 O │ │ │ 01_Cost_Function.txt
" _+ Q/ j; M$ [4 C │ │ │ 02_Backpropagation_Algorithm.mp4
$ p! ]4 L, B7 j" [ │ │ │ 02_Backpropagation_Algorithm.srt4 S( z4 z( z; N) {
│ │ │ 02_Backpropagation_Algorithm.txt/ M. `+ F j9 ^, H% m
│ │ │ 03_Backpropagation_Intuition.mp4) v) F) o1 ~4 E. g
│ │ │ 03_Backpropagation_Intuition.srt
) Y9 P0 t) o! u! B │ │ │ 03_Backpropagation_Intuition.txt7 ]- e& h% G( {3 Z: g$ Q' T5 O0 ?
│ │ │ 05_Gradient_Checking.mp4$ p$ M9 Z( m M0 L
│ │ │ 05_Gradient_Checking.srt3 k& y( I; k) ]2 b7 M; M2 l
│ │ │ 05_Gradient_Checking.txt
* ?; u0 V3 W+ ?6 {; b% G; @& }* i │ │ │ 06_Random_Initialization.mp4: V7 _2 l3 K; J
│ │ │ 06_Random_Initialization.srt
8 ~, _/ l8 K3 j$ }4 q5 k0 r0 b │ │ │ 06_Random_Initialization.txt
9 ]) b& p" N& G: B& G │ │ │ 07_Putting_It_Together.mp4" O @8 ]+ _3 S6 J
│ │ │ 07_Putting_It_Together.srt
) ~, W# _! v: C │ │ │ 07_Putting_It_Together.txt1 ^6 U0 U% u* i( T
│ │ │ 08_Autonomous_Driving.mp46 K+ O# V1 T) ~! M% ^$ m
│ │ │ 08_Autonomous_Driving.srt
2 s+ T, A" l, t+ U9 c1 Q6 I │ │ │ 08_Autonomous_Driving.txt0 g0 [9 l/ b T, ]$ j5 l. I! |8 ?
│ │ │ ! ~6 \0 Q L. `8 y& j3 E4 s+ o- d" B
│ │ ├─10_X._Advice_for_Applying_Machine_Learning
# s# z9 L* ~- }" T" @ │ │ │ 06_Learning_Curves.mp4
5 T- q) n! P5 K9 ?$ X │ │ │ 06_Learning_Curves.srt
K( d. a8 u/ e" f- m │ │ │ 06_Learning_Curves.txt9 S: q& X5 P+ p4 @ i/ _
│ │ │ 7 V W' z& y% P1 H
│ │ ├─11_XI._Machine_Learning_System_Design( Z# i4 R) A2 K( T: E
│ │ │ 02_Error_Analysis.mp4: I l0 ?6 c; D% t s' w
│ │ │ 02_Error_Analysis.srt, d# X0 P- M2 j d3 x4 G
│ │ │ 02_Error_Analysis.txt5 C9 w8 _5 I) [! P7 {' p
│ │ │ ) P4 V M$ O$ y
│ │ ├─12_XII._Support_Vector_Machines
- ^% H% F4 H" t! o2 |& @8 }! H: O: B │ │ │ 01_Optimization_Objective.mp4
1 o, @1 g$ K D2 [: {7 O │ │ │ 01_Optimization_Objective.pdf
2 _' }5 y1 z. C+ ~! x │ │ │ 01_Optimization_Objective.pptx- w3 j( e1 n- d0 z
│ │ │ 01_Optimization_Objective.srt
, G* r+ T2 a( S) p │ │ │ 01_Optimization_Objective.txt
* h6 f0 n* K. e. W │ │ │ 02_Large_Margin_Intuition.mp4' N8 ~- m# _; K$ J: B
│ │ │ 02_Large_Margin_Intuition.srt/ l9 y" p0 K) x5 m4 i0 C
│ │ │ 02_Large_Margin_Intuition.txt" k U/ F+ D! D- E3 S' ?$ E2 ~
│ │ │ 04_Kernels_I.mp4& `0 p% W- j" a0 _% B' @
│ │ │ 04_Kernels_I.srt
! s S) F* J/ w │ │ │ 04_Kernels_I.txt5 c' U9 O) X& g, u8 o g& y5 T
│ │ │ 05_Kernels_II.mp4
2 j0 A! a7 p; D │ │ │ 05_Kernels_II.srt/ ~/ H4 H }: y" C! `
│ │ │ 05_Kernels_II.txt
5 I& }6 R: i3 P │ │ │ 06_Using_An_SVM.mp4
' R9 S' J1 H1 V1 y& w │ │ │ 06_Using_An_SVM.srt' ^( y: Y! q4 ^/ g2 m+ Z
│ │ │ 06_Using_An_SVM.txt* N6 R8 P5 [" n* d1 }& G9 T$ g
│ │ │
" V( @; c. A6 L0 u- z, A% ^1 r │ │ ├─13_XIII._Clustering
$ p6 u2 |/ @4 F F │ │ │ 01_Unsupervised_Learning-_Introduction.mp4
' E E ~, T5 E/ o) ~- u │ │ │ 01_Unsupervised_Learning-_Introduction.pdf! q. c5 l9 N1 T Y6 c
│ │ │ 01_Unsupervised_Learning-_Introduction.pptx
6 w8 f, ^8 G# }- M$ ~; y │ │ │ 01_Unsupervised_Learning-_Introduction.srt- z1 y2 g# x' l8 e5 y' Z
│ │ │ 01_Unsupervised_Learning-_Introduction.txt
* F0 M+ u8 H! Z │ │ │ 02_K-Means_Algorithm.mp47 a+ V1 T5 o# d0 a0 G$ D+ h
│ │ │ 02_K-Means_Algorithm.srt
9 u) Q( y& @! C │ │ │ 02_K-Means_Algorithm.txt
4 n, P1 v/ \" J- m │ │ │ 03_Optimization_Objective.mp4
7 N6 _8 l4 p7 I: Z │ │ │ 03_Optimization_Objective.srt
& w7 A1 }9 W. Y5 y │ │ │ 03_Optimization_Objective.txt
$ V/ x' h" D7 Q/ ^ │ │ │ 04_Random_Initialization.mp4
( e6 D( @; R; w0 @6 u1 e │ │ │ 04_Random_Initialization.srt
3 |# z4 y+ X1 p$ J4 ] │ │ │ 04_Random_Initialization.txt
" i% ]2 u* W+ ]- f8 O │ │ │ 05_Choosing_the_Number_of_Clusters.mp46 d( d8 T( M6 r. {- T
│ │ │ 05_Choosing_the_Number_of_Clusters.srt3 P: {4 p- y4 S& j
│ │ │ 05_Choosing_the_Number_of_Clusters.txt
/ a2 H- y' h8 z4 ?8 x+ @1 C4 _+ O │ │ │ & Z0 e' U0 @, t2 ?' q' ?! F& H1 W
│ │ ├─14_XIV._Dimensionality_Reduction+ G( [" w. R7 B" h) z" ^
│ │ │ 02_Motivation_II-_Visualization.mp40 b9 Q3 g! |3 x/ D9 o) |
│ │ │ 02_Motivation_II-_Visualization.srt
' f7 L* a, {8 a- X │ │ │ 02_Motivation_II-_Visualization.txt
5 C5 E# {1 V* A+ l! U' ]8 [: D, d │ │ │ 07_Advice_for_Applying_PCA.mp4
T# j; d5 Y1 ~+ k. O3 c7 K; k# r │ │ │ 07_Advice_for_Applying_PCA.srt
- R" O) W4 T& |0 P7 U │ │ │ 07_Advice_for_Applying_PCA.txt9 k% Y( l) u' W. u7 g7 Z
│ │ │
1 E. l" k Q; b+ \. @6 p- ] │ │ ├─15_XV._Anomaly_Detection8 n/ z" c! z I, k
│ │ │ 01_Problem_Motivation.mp4 Q. ~8 Q* V) G
│ │ │ 01_Problem_Motivation.pdf
- C4 u) Q& X9 F% o/ s, t6 R" m │ │ │ 01_Problem_Motivation.pptx
) R6 j7 c- p: p" g' I- q, f │ │ │ 01_Problem_Motivation.srt
0 n# J1 ]0 ~; z. k/ b4 } │ │ │ 01_Problem_Motivation.txt5 s, Z2 s' Z0 A3 Y. `
│ │ │ 02_Gaussian_Distribution.mp4
" l: J4 h: V* [# [5 { │ │ │ 02_Gaussian_Distribution.srt
7 P, c: _% Q0 z- l │ │ │ 02_Gaussian_Distribution.txt
: y* { v6 T: \% `( r" T6 S8 k │ │ │ 03_Algorithm.mp4
9 u; X* r( y( E% J │ │ │ 03_Algorithm.srt
7 c' x; y+ s$ m& G" b, n │ │ │ 03_Algorithm.txt
; b( y# V) {6 Z: |$ U) ^. d │ │ │ 06_Choosing_What_Features_to_Use.mp47 h5 L6 q: }6 x, M
│ │ │ 06_Choosing_What_Features_to_Use.srt& ^6 q) A8 }% @! m4 Z# E1 R4 Y
│ │ │ 06_Choosing_What_Features_to_Use.txt
" U# x6 P1 P, S │ │ │ 7 Z. ]; C" a# H' E
│ │ ├─16_XVI._Recommender_Systems
! \! u; s1 {7 W" E$ s │ │ │ 01_Problem_Formulation.mp4& G7 y( r# y0 A% t$ s3 D* a! m
│ │ │ 01_Problem_Formulation.pdf
. \4 \6 `, ~) q9 C, e │ │ │ 01_Problem_Formulation.pptx
% M# c: i, H! D1 @5 u │ │ │ 01_Problem_Formulation.srt9 y. z! N, K! m3 [. \2 o" q& ^1 d
│ │ │ 01_Problem_Formulation.txt0 m h4 v$ T/ W- y; Q
│ │ │ 02_Content_Based_Recommendations.mp4( [. ~$ o) O/ H3 Y: Z* x
│ │ │ 02_Content_Based_Recommendations.srt
+ ]; s7 @. t' Z c1 f% D* v │ │ │ 02_Content_Based_Recommendations.txt
: O* G* X2 v# p' {, e' B │ │ │ 03_Collaborative_Filtering.mp43 L2 q1 Z6 w- g$ [* h+ w C
│ │ │ 03_Collaborative_Filtering.srt
/ w' H' X8 I8 G: \ │ │ │ 03_Collaborative_Filtering.txt
$ Y& a3 ~7 J3 y K+ |' Z. b* g9 @ │ │ │ 04_Collaborative_Filtering_Algorithm.mp4) e( D3 f, y/ q) t
│ │ │ 04_Collaborative_Filtering_Algorithm.srt
8 M2 `. ]9 m- @$ n7 ]5 d' d │ │ │ 04_Collaborative_Filtering_Algorithm.txt
* x) E' l4 |5 E8 a8 O8 a │ │ │
/ K/ _7 r1 ?* ]" F7 Y- R# c │ │ ├─17_XVII._Large_Scale_Machine_Learning: y( M* f1 D: c( c4 d, W7 e
│ │ │ 05_Online_Learning.mp4! ~# t0 G" k( ]' h$ z% z
│ │ │ 05_Online_Learning.srt
$ H, ~1 ^) p+ X# d6 ~0 K8 w │ │ │ 05_Online_Learning.txt5 @- S3 B- @4 C! N( y& ~' n0 |7 H
│ │ │ ! G0 m4 ^# \7 \" ^& `1 g# C o$ f4 H
│ │ ├─18_XVIII._Application_Example-_Photo_OCR/ n6 k6 a" G4 F; [- s0 l: L3 B1 b
│ │ │ 02_Sliding_Windows.mp47 o, `4 u% s5 x) [4 }! n
│ │ │ 02_Sliding_Windows.srt
! X/ ` c+ n, T8 a l │ │ │ 02_Sliding_Windows.txt* K& G, b$ s/ V9 c% p
│ │ │ ; l- U6 }+ T$ o6 l
│ │ └─19_XIX._Conclusion
) I& T) {, f+ }6 P) C0 C │ │ 01_Summary_and_Thank_You.mp4
7 E1 b/ V( B" J │ │ 01_Summary_and_Thank_You.srt7 ~% C8 I/ |; H: B) P
│ │ 01_Summary_and_Thank_You.txt2 q9 z" |) e/ Z% L+ \
│ │ 1 I% @' J0 z+ A' b, A0 A- n& L
│ └─机器学习-斯坦福-Andrew NG-2012
+ S: a2 f1 }0 R5 [% x) L$ X │ │ 下载说明.txt w: j5 k4 q* M- k$ w" @0 z7 u
│ │ 关注我们.png
: U7 z' f- G5 f% H │ │ 攻城狮论坛=网络技术+编程视频.url/ ?; k2 u8 U1 {, T; ?
│ │ 机器学习笔记2012_v0.1.pdf: y# w4 n7 O0 F& y, ^: q
│ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
: G+ G% q: }4 n! _# V3 G │ │
8 U5 A( R- U; R │ ├─week 1, F; z: n6 X. q9 V _, a; q' O$ }' e1 a
│ │ 1 - 1 - Welcome (7 min).mp4
. Q& k+ _; }% c3 F8 _ │ │ 1 - 1 - Welcome (7 min).srt
2 B% O2 D9 N7 U$ Q │ │ 1 - 2 - What is Machine Learning (7 min).mp49 I: l+ L( [8 s4 r' }, V" g
│ │ 1 - 2 - What is Machine Learning- (7 min).srt
; ]% `6 [& V# r$ J; w( l │ │ 1 - 3 - Supervised Learning (12 min).mp4
3 y; H8 v! z. I. {7 W5 S( }3 Z │ │ 1 - 3 - Supervised Learning (12 min).srt
/ V& y; A9 s& i) h( k2 j. V │ │ 1 - 4 - Unsupervised Learning (14 min).mp4+ i8 m5 B1 _& O; I
│ │ 1 - 4 - Unsupervised Learning (14 min).srt
8 U) n5 I/ I f │ │ 2 - 1 - Model Representation (8 min).mp4% N7 X# B f* z- x+ O( H# c; {
│ │ 2 - 1 - Model Representation (8 min).srt
4 M9 ]7 ~: M* g │ │ 2 - 2 - Cost Function (8 min).mp4
% X2 }9 F: Y4 W& u' W" [: ? │ │ 2 - 2 - Cost Function (8 min).srt/ ]" b8 [" d% ]3 P0 s ]
│ │ 2 - 5 - Gradient Descent (11 min).mp4% E# a* h; ]4 @5 s T( |8 ^" t
│ │ 2 - 5 - Gradient Descent (11 min).srt+ C- V4 F# _* D
│ │ 2 - 8 - What's Next (6 min).srt0 k1 a% ~: h5 Q
│ │ 2 - 8 - Whats Next (6 min).mp49 a' H1 [3 h2 x; B
│ │ 3 - 1 - Matrices and Vectors (9 min).mp4
' j6 M% m: d# K4 V │ │ 3 - 1 - Matrices and Vectors (9 min).srt
3 N6 }/ s9 ^+ |9 O" i6 A │ │
& d! \# s0 J' y( S) v, ? │ ├─week 10 f- Q0 M% u6 q' W
│ │ 17 - 5 - Online Learning (13 min).mp4! _6 q6 v; x7 Y3 m0 e2 {- r1 r
│ │ 17 - 5 - Online Learning (13 min).srt# j# J; r0 g1 A: x% ^( x
│ │ Lecture17.pdf# w5 d3 W9 o& T; A. z
│ │ % l8 I5 U e7 @8 z, ]/ F; B
│ ├─week 2" o- F0 S* K8 a
│ │ 4 - 1 - Multiple Features (8 min).mp4* l- g' d7 p; H/ t! ~$ L" X: B
│ │ 4 - 1 - Multiple Features (8 min).srt5 ~% q ]$ W& G0 c* O+ n& `
│ │ 4 - 6 - Normal Equation (16 min).mp4: B: j: b( m% T0 @
│ │ 4 - 6 - Normal Equation (16 min).srt$ d7 V- ~1 h' @( R7 \9 e4 N) {; A- `
│ │ 5 - 1 - Basic Operations (14 min).mp44 @& P9 M5 @8 C
│ │ 5 - 1 - Basic Operations (14 min).srt
. h/ ~% j1 @8 C) D2 ~/ p; r0 d6 N │ │ 5 - 2 - Moving Data Around (16 min).mp4& p8 T% [7 g2 T" m
│ │ 5 - 2 - Moving Data Around (16 min).srt
% ] i8 {2 _( C' X │ │ 5 - 3 - Computing on Data (13 min).mp4
) A/ x1 F2 z9 T1 n* J; q3 w F │ │ 5 - 3 - Computing on Data (13 min).srt
+ k0 ~6 A2 m0 R │ │ 5 - 4 - Plotting Data (10 min).mp4% j/ m3 D3 X( g% S$ Y7 [
│ │ 5 - 4 - Plotting Data (10 min).srt! n- A2 I* h' `$ N( N
│ │ 5 - 6 - Vectorization (14 min).mp4
- j9 z$ k1 W" K │ │ 5 - 6 - Vectorization (14 min).srt3 ]/ M! D; y3 j- K
│ │ Lecture4.pdf. K6 ]9 r- u) y! R& [0 f
│ │ Lecture5_octave tutorial.pdf
9 i% P8 u/ d$ q$ ^. ]9 p+ R3 e0 E │ │
0 r9 z3 Q) i- x. r$ Z │ ├─week 3' ~5 O. S G5 B
│ │ 6 - 1 - Classification (8 min).mp4
9 K# V: R0 s- ]8 X* p │ │ 6 - 1 - Classification (8 min).srt- h4 ^1 z% k& ^% g3 P8 I
│ │ 6 - 2 - Hypothesis Representation (7 min).mp4
. q9 T N" P2 U │ │ 6 - 2 - Hypothesis Representation (7 min).srt
% u6 Q0 C0 V/ n. k% N% P$ m D J │ │ 6 - 3 - Decision Boundary (15 min).mp4
, v o6 r$ Q. F- j │ │ 6 - 3 - Decision Boundary (15 min).srt
8 ~2 y' u- B/ B │ │ 6 - 4 - Cost Function (11 min).mp4
; b: R+ t- P: A+ S- ^0 h# J: O │ │ 6 - 4 - Cost Function (11 min).srt- ?1 v" g" e& B: p5 s% C
│ │ 6 - 6 - Advanced Optimization (14 min).mp4# E& o, u/ q3 ~; A- Y: U9 a
│ │ 6 - 6 - Advanced Optimization (14 min).srt
: [+ X6 ^" z7 P5 h │ │ 7 - 2 - Cost Function (10 min).mp4
: k y) U4 Y/ E0 D5 z- H │ │ 7 - 2 - Cost Function (10 min).srt( W6 T: k$ L% ?, u
│ │ docs_slides_Lecture6.pdf! q3 k+ Z* @; J5 K6 d( T
│ │ docs_slides_Lecture7.pdf7 A+ V3 {, f1 d. C* q; W
│ │
3 d) L' h3 C- Y- R/ ^* {3 [& L% e# g │ ├─week 4# v0 i9 k9 p# Z% m8 B# m8 D) E3 l
│ │ 8 - 1 - Non-linear Hypotheses (10 min).mp45 K: r& w- s* j/ V/ P
│ │ 8 - 1 - Non-linear Hypotheses (10 min).srt
8 j8 o& [6 _; p; v! V8 W │ │ 8 - 2 - Neurons and the Brain (8 min).mp49 N- ^ K7 R+ [# D
│ │ 8 - 2 - Neurons and the Brain (8 min).srt, r% L3 y. n- R9 o& ^
│ │ 8 - 3 - Model Representation I (12 min).mp4
/ C* K7 J; K. G( p& a' \3 y │ │ 8 - 3 - Model Representation I (12 min).srt
* i' X" }$ O( y1 h4 \- T │ │ 8 - 4 - Model Representation II (12 min).mp4
* m" P! s* U y │ │ 8 - 4 - Model Representation II (12 min).srt
' b1 P/ N) }& v! R5 H │ │ 8 - 5 - Examples and Intuitions I (7 min).mp4
0 ~3 C4 M$ e2 S; }0 u │ │ 8 - 5 - Examples and Intuitions I (7 min).srt0 R' X3 O& n- E: x$ u4 e5 A
│ │ 8 - 7 - Multiclass Classification (4 min).mp4
* q' d" }* ^" T │ │ 8 - 7 - Multiclass Classification (4 min).srt. f( B' |* _" f A6 F G9 Q* ^
│ │ docs_slides_Lecture8.pdf# G/ R$ v& ^, P: o
│ │
! A7 p$ z" m, b5 T3 p+ k( P │ ├─week 5
& `7 K9 h1 G2 f │ │ 9 - 1 - Cost Function (7 min).mp4
1 s- m; e, N% { │ │ 9 - 1 - Cost Function (7 min).srt
5 X4 Q5 d" e, r/ p4 D9 b │ │ 9 - 5 - Gradient Checking (12 min).mp4
' Y7 d$ s3 _, n) {5 g │ │ 9 - 5 - Gradient Checking (12 min).srt3 J: {7 J$ ?, e" e/ {3 k
│ │ 9 - 6 - Random Initialization (7 min).mp43 m; G" A" c, b. s' `9 h
│ │ 9 - 6 - Random Initialization (7 min).srt* n, _$ Z; S4 H+ R. g7 e$ m' b
│ │ 9 - 7 - Putting It Together (14 min).mp4
" y8 F' w; l; \# S' i9 w! x │ │ 9 - 7 - Putting It Together (14 min).srt
, ~/ E0 L/ X9 M+ _9 j& ^, d- Q8 c │ │ 9 - 8 - Autonomous Driving (7 min).mp4
" ?9 z/ r, P% S5 \1 v │ │ 9 - 8 - Autonomous Driving (7 min).srt
: S- e6 C( n, w │ │ docs_slides_Lecture9.pdf
* b2 |5 G1 N6 [7 y/ \7 {0 w │ │
" M2 v) j; r) S! e% J1 r2 g │ ├─week 6
' L0 s. l k8 Y U/ f( I& [ │ │ 10 - 2 - Evaluating a Hypothesis (8 min).mp4- H. A4 j2 [. h: B3 U
│ │ 10 - 2 - Evaluating a Hypothesis (8 min).srt" P! g" t! y. h5 L9 G0 N
│ │ 10 - 6 - Learning Curves (12 min).mp4
" D- h; o* W; R" m4 d' v. l │ │ 10 - 6 - Learning Curves (12 min).srt! v; Z3 L' t" |8 U- ?
│ │ 11 - 2 - Error Analysis (13 min).mp45 w0 e* N: q1 z7 \) Z7 ]
│ │ 11 - 2 - Error Analysis (13 min).srt& ^% x- A4 _6 s, r( X
│ │ docs_slides_Lecture10.pdf
- i0 f4 i- O, O# l9 o3 h$ R2 j8 B: k │ │ 9 o) J! T6 B- K+ r
│ ├─week 74 B( ]; `2 }) a2 b2 Q( D9 A8 J
│ │ 12 - 1 - Optimization Objective (15 min).mp4
" f( ?# P! V7 J │ │ 12 - 1 - Optimization Objective (15 min).srt9 C, L g; b( p; x6 _5 y' d) G
│ │ 12 - 2 - Large Margin Intuition (11 min).mp49 e$ z6 T+ F D$ K1 B3 A8 `
│ │ 12 - 2 - Large Margin Intuition (11 min).srt A' P5 G6 x0 _' T6 s9 v
│ │ 12 - 4 - Kernels I (16 min).mp4
& p4 ~) u$ y8 ] │ │ 12 - 4 - Kernels I (16 min).srt
6 ?8 H% [5 F0 C3 Y+ R$ x │ │ 12 - 5 - Kernels II (16 min).mp4! p7 F7 \ {( h' O7 `% O
│ │ 12 - 5 - Kernels II (16 min).srt
1 _% b4 K% w' \2 E: T) e8 z9 Y │ │ 12 - 6 - Using An SVM (21 min).mp4( j8 d$ d' n% C# ]
│ │ 12 - 6 - Using An SVM (21 min).srt
" N9 f2 c; r7 y │ │ docs_slides_Lecture12.pdf
7 I6 [% E* w/ g: @ │ │ - U5 |* t) ]' j$ b
│ ├─week 85 W3 w- D1 k7 f
│ │ 13 - 2 - K-Means Algorithm (13 min).mp4
, b% Y6 x' g* [) ?% h7 E │ │ 13 - 2 - K-Means Algorithm (13 min).srt
; Z1 c4 B5 }0 N T │ │ 13 - 3 - Optimization Objective (7 min).mp4
5 o; j3 b; G$ M# g │ │ 13 - 3 - Optimization Objective (7 min).srt
% I4 o* j3 t6 \" e) {3 l; n │ │ 13 - 4 - Random Initialization (8 min).mp4
6 j) ]6 m0 W" u# \: H4 f- k │ │ 13 - 4 - Random Initialization (8 min).srt
7 @# H- o: d ]4 t# ~ │ │ 14 - 7 - Advice for Applying PCA (13 min).mp4# q$ x# P4 Z8 ^9 o
│ │ 14 - 7 - Advice for Applying PCA (13 min).srt
6 \. n7 d. k! I u x+ c │ │ docs_slides_Lecture13.pdf0 j) ?% b5 W6 j9 R1 `
│ │ docs_slides_Lecture14.pdf
9 J9 M6 Y, N5 V8 U4 n │ │
# w3 H4 y+ f: J. {7 g8 i │ ├─week 9
, A* L; K0 j& c" `" u │ │ 15 - 1 - Problem Motivation (8 min).mp4# X1 s+ A; ^" z" b5 J
│ │ 15 - 1 - Problem Motivation (8 min).srt, K7 g! n3 S* ^9 |
│ │ 15 - 2 - Gaussian Distribution (10 min).mp4
% y1 P+ N D$ o% A) a) _' x0 m0 U. P │ │ 15 - 2 - Gaussian Distribution (10 min).srt. C1 ]8 }' i+ N6 L
│ │ 15 - 3 - Algorithm (12 min).mp4
, |) L) [: t+ I# \4 ?: r- Y │ │ 15 - 3 - Algorithm (12 min).srt/ a( G) u m1 i& G
│ │ 16 - 1 - Problem Formulation (8 min).mp4
+ A; S7 _: y8 p │ │ 16 - 1 - Problem Formulation (8 min).srt
5 s8 e6 p. r6 u6 w │ │ 16 - 3 - Collaborative Filtering (10 min).mp4
; z- |$ Z, o- P* O │ │ 16 - 3 - Collaborative Filtering (10 min).srt
! x5 b6 |8 ~3 ?0 d% L6 t │ │ docs_slides_Lecture15.pdf
! L- i2 K; h8 o" ~7 |* T0 x │ │ docs_slides_Lecture16.pdf
8 h% X- v5 V, W1 M8 c+ m │ │
! g. X- ~( O$ w% [1 c# S │ └─作业
# i5 w! i7 X: W; [" J* s │ │ ex1.zip
) Y2 E! {0 O S$ O& \' ] │ │ ex3.zip2 S& H0 Y& f2 S( T. d! r: k- a
│ │ ex4.zip' u7 V1 r ^5 u g+ @+ Q; d' r
│ │ ex5.zip. R8 ~2 W- u, y" c
│ │ ex6.zip* h3 k# Y" N) L$ o$ ^: N+ G( _
│ │ ex7.zip, L8 F2 `- T5 k6 B7 A- Q
│ │ ex8.zip7 u! M m0 [7 ]& d
│ │ Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe
T! u" u* r C9 h8 p/ y# q │ │ , p) r( H' ]( ?; N F
│ └─答案9 ?* D: [& I. ` o# e
│ ├─ex1
6 q1 c6 B" Z. N4 l' { │ │ computeCost.m
- i6 F1 ?2 {! I │ │ computeCostMulti.m
4 \* ^7 a% v8 }5 g+ d7 A7 m │ │ ex1.m* l6 d; s( h5 S; i$ L, e: j
│ │ ex1data1.txt+ ]8 N* Z! I5 A* l
│ │ ex1data2.txt
: _. r- j. f0 t. z$ \3 w │ │ ex1_multi.m5 P. O3 f9 s4 p A# r1 f
│ │ featureNormalize.m
" H4 ]/ K( x: s( b; P/ V& Y │ │ gradientDescent.m, a: M6 _" b% |$ t
│ │ gradientDescentMulti.m& B2 b6 T7 G* I: d) k
│ │ ml_login_data.mat o0 N6 t& j ^+ j' [9 Z/ w2 o
│ │ normalEqn.m
9 `7 S$ F5 T# \; }, H │ │ plotData.m1 A; A, i# B& }. [% z
│ │ submit.m
! u, F; y# o; W9 c& D; U │ │ submitWeb.m3 o Y/ S9 w4 {" e6 U: j9 H9 ~
│ │ warmUpExercise.m
8 v2 m! `+ v6 u# z4 B │ │
2 Q5 P; b* U' C │ ├─ex2! ~5 k# R# ` t- J
│ │ costFunction.m2 n3 {$ z! m9 @. m. f
│ │ costFunctionReg.m- X! K( g% N5 T: {0 [6 }# R) z
│ │ ex2.m
! S' [- h p7 _$ T2 l% n │ │ ex2data1.txt
! ^+ Y# X3 H; z# i- h: E │ │ ex2data2.txt2 o" y) @; T2 n" }' P
│ │ ex2_reg.m
, ], C7 M! j: ]; ^6 b- @ │ │ mapFeature.m
( `5 V0 d6 n" H) p! O │ │ ml_login_data.mat) C& u _* y5 E5 q
│ │ plotData.m) c+ h7 S5 ]* M
│ │ plotDecisionBoundary.m0 x8 |. O3 |. e" H" j# \
│ │ predict(1).m.baiduyun.downloading0 q8 e, q! j, q
│ │ predict.m
4 v* p7 w: X* h5 i* I. x3 h │ │ sigmoid.m P0 G- k& a. x
│ │ submit(1).m.baiduyun.downloading2 q( ]* i; l8 ]0 c5 W, ^0 E# T$ F2 o
│ │ submit.m8 M9 i" h3 ]+ e e$ O% b/ O; U2 v
│ │ submitWeb(1).m.baiduyun.downloading
% ?$ ^; b6 t2 z3 v, V │ │ submitWeb.m
2 F) n/ I& t! S" B8 F │ │
, W/ K, t$ t& k5 j6 a8 |6 Y │ ├─ex3
; Y# X# a4 b" h; I! Z │ │ displayData.m
4 T$ j+ _3 R# T8 S │ │ ex3(437).m.baiduyun.downloading
. W0 I+ N! Q$ x' F/ R │ │ ex3.m! j0 w2 j# w$ m; J1 r ]
│ │ ex3data1.mat
+ u/ l$ d9 |( G% l1 U. m. K │ │ ex3weights.mat2 b& A5 `( B4 D3 A
│ │ ex3_nn.m
# e8 D3 Q- @( S& a. S0 K* e1 m+ n │ │ fmincg.m# D. k, m: B& l4 G; Q+ p. j5 ~
│ │ lrCostFunction.m
% I ^+ Z7 n" n' ~ │ │ ml_login_data.mat
7 L, \* r) C: c( Q6 Q0 Z) J, l │ │ oneVsAll.m
, m9 E. k! ]' E- B; C2 C9 G │ │ predict.m" z4 H0 t$ D( { U
│ │ predictOneVsAll.m
2 f2 C% H+ W& f5 W/ [, S │ │ sigmoid.m& `# O+ Y4 I$ I$ n
│ │ submit.m
, P h$ l1 S3 D1 @. @ │ │ submitWeb.m, x2 ~& ~" q# D2 _! f6 d4 \
│ │
1 b! P' I# N' U8 W │ ├─ex4
) q/ ~! n- T% J# H/ T4 W │ │ checkNNGradients.m# E$ X: T3 n# q$ D( R5 w
│ │ computeNumericalGradient.m
) t" Y1 y I, w8 r" K │ │ debugInitializeWeights.m
4 _4 V( R/ x5 Z8 G" ~ │ │ displayData.m' \5 `! A7 @) h: p
│ │ ex4.m5 ?0 O0 J% ?1 h8 l1 h
│ │ ex4data1.mat# C2 I, I5 }: U4 f2 S
│ │ ex4weights.mat
+ b9 `% o6 X/ O% V4 \: ^ │ │ fmincg.m% d! y& V' F0 D
│ │ ml_login_data.mat1 ~+ x& y1 X# o6 ^0 M# z
│ │ nnCostFunction.m' Z; a+ s/ S8 b$ F4 [+ }
│ │ predict.m
4 O6 b$ f/ Q9 H( S) l; r │ │ randInitializeWeights.m+ i6 o% D B3 C. V( {7 v
│ │ sigmoid.m
" Z) F! F0 E/ p. {$ J' i │ │ sigmoidGradient.m
: }' r3 e M( h4 h │ │ submit.m
! L5 ?( R3 X, S V │ │ submitWeb.m
" l/ i8 m: n# ]( C: b! E v! F │ │ , w3 r4 K2 z9 m) F5 q: O
│ ├─ex5
- W: z; M9 m, S" q9 u1 f │ │ ex5.m
# k0 C) h; g, j# g: w6 A │ │ ex5data1.mat" Y/ J, |) f6 }1 ]; a3 w/ P: w: u
│ │ featureNormalize.m
9 G5 L# {' W! n │ │ fmincg.m Z, W. V3 }; x
│ │ learningCurve.m
t$ J7 B$ Y# Z3 K' N │ │ linearRegCostFunction.m2 Y8 F; e: J, E6 ]
│ │ ml_login_data.mat3 \) A1 m- q% L* e6 O* |
│ │ plotFit.m7 E! Z% H Z2 ]( K2 [" _0 J+ P' [) G
│ │ polyFeatures.m* F; {4 b) u8 `/ w$ o
│ │ submit.m& @% m6 w' z4 F; U$ W+ B2 K# j4 A
│ │ submitWeb.m
# K# B( i/ N) O9 ?( T+ R │ │ trainLinearReg.m. y' i+ q: z+ ] T- C: U
│ │ validationCurve.m' S. _# _# C8 l5 }& H6 c6 }$ C
│ │
9 ^: A' _& f1 i. g5 y │ ├─ex6- K! K+ u4 g8 ?/ m/ e0 X9 F8 l
│ │ dataset3Params.m
2 p9 V" m5 k8 k& v │ │ emailFeatures.m+ y5 V+ s$ |* @2 `- ^: O
│ │ emailSample1.txt& m6 k( @0 a+ `9 Y
│ │ emailSample2.txt
k0 B& L& s6 P( h! a% f# O │ │ ex6.m& n! ?9 {' x+ y. z' b7 O/ O
│ │ ex6data1.mat
2 i2 u; f3 e5 u/ ? │ │ ex6data2.mat
B, ?: } T& h2 W5 v: e3 A │ │ ex6data3.mat
9 n! K5 r& R: |8 b1 h0 A- \, y3 _ │ │ ex6_spam.m
9 Y) } O7 o, }; n │ │ gaussianKernel.m
; J" {9 G4 d! w5 T0 z1 I │ │ getVocabList.m
0 T7 z7 E7 R7 N │ │ linearKernel.m
# Y' v' ?# u2 K; _& v& p- J0 B │ │ ml_login_data.mat
% d4 i. r: ?1 M; U4 p │ │ octave-core
2 E: t; X0 s( `! X. O4 M │ │ plotData.m+ D. f7 f& b! `5 l+ \; ?( z& z
│ │ porterStemmer.m$ B0 J' W; D) C
│ │ processEmail.m
( f3 t+ m1 G Z% u, p │ │ readFile.m: F% h$ ^6 G% U0 w# y
│ │ spamSample1.txt
. [6 x7 X7 X9 p4 C" S9 N* P │ │ spamSample2.txt) N$ O; {8 C2 W m: w! C
│ │ spamTest.mat
: w+ i) r! c. u% _; Y# I% d │ │ spamTrain.mat9 K+ R1 m6 H0 H) l, T
│ │ submit.m
5 q4 ~; m% P4 s0 J7 C │ │ submitWeb.m
: A6 w1 P/ `3 i* c. u+ P, G; j; o, ] │ │ svmPredict.m
4 w9 p( i, ]3 p │ │ svmTrain.m
4 g1 B7 W- j1 e5 K │ │ visualizeBoundary.m
- q/ e7 ?+ {/ o) X │ │ visualizeBoundaryLinear.m9 C( P' r5 c4 S0 n% i+ m( e8 B" u
│ │ vocab.txt: }. T$ D/ M+ E% ]7 W
│ │ ! s5 a* ^2 I" a; J( G" q+ l0 h
│ ├─ex7/ E$ E; r- [7 M; }
│ │ bird_small.mat
/ |. x3 ?" y: s6 A. \ │ │ bird_small.png
, ~. G( e( B1 X │ │ computeCentroids.m1 a) K! ]8 D2 F5 d1 r
│ │ displayData.m" l: _& |9 I2 d
│ │ drawLine.m6 P6 f& I' q8 L# C0 h
│ │ ex7.m
" H4 S4 C7 k- \' h5 Z │ │ ex7data1.mat0 C/ W' |/ [( s# C
│ │ ex7data2.mat
* A% J1 I; R6 \- @5 ^7 z2 o# W6 ^ │ │ ex7faces.mat: h' Y' G8 Y. n! J
│ │ ex7_pca.m
f' r( c, k+ M0 U; u3 j0 C5 u6 L │ │ featureNormalize.m
9 V2 i/ b, l; T/ `" T │ │ findClosestCentroids.m7 j1 l1 N- A; V, H; ]
│ │ kMeansInitCentroids.m9 s6 D" K: m4 {' Q: R
│ │ ml_login_data.mat! y+ ]3 E0 j4 r5 e c8 D" [
│ │ octave-core7 `8 H) N' n% X h% p* U
│ │ pca.m+ D, H$ R) I; i$ D4 S
│ │ plotDataPoints.m
- w: b8 w: d# U+ r! h8 ?8 p │ │ plotProgresskMeans.m
" J4 q; M. g2 {4 t( \ │ │ projectData.m" y0 A6 G, \6 D7 {
│ │ recoverData.m0 }8 C6 m6 K5 U, W9 F/ d& C
│ │ runkMeans.m9 k4 i) z8 I" C1 \. h( ~+ P
│ │ submit.m7 f& g: _/ o( i+ z$ L
│ │ submitWeb.m
% |2 e( _* _4 ]& G6 I) v │ │
3 ?/ G8 Y& g8 c% t# D │ └─ex8& g' _% O$ d* z$ O
│ checkCostFunction.m% D6 ?5 o& q" D1 U8 a- M8 q
│ cofiCostFunc.m8 M/ R5 @( P7 ]3 V+ r
│ computeNumericalGradient.m" _' ?; O+ ]" G0 d
│ estimateGaussian.m' B6 q; G/ u/ g$ v; {# _
│ ex8.m
- d. R" q* I# w) w; V7 d) k │ ex8data1.mat0 Q' X- ] B! C
│ ex8data2.mat
/ o% j; x8 [6 f; o0 N R$ L │ ex8_cofi.m& p7 t8 g# k& o) d) `" X( g0 h- `' n
│ ex8_movieParams.mat" _/ k6 d. Y& |% }( z6 c! {1 A
│ ex8_movies.mat0 s* T( H& e/ g' M% Y0 }; d6 |
│ fmincg.m
8 _4 t, T$ B- I# |) p3 P2 h$ i& w │ loadMovieList.m
! |- K8 W+ x% z9 P5 c3 ` │ ml_login_data.mat& `- |5 D( \( B
│ movie_ids.txt+ Z2 p! h% m: T7 Z, x0 ?5 H
│ multivariateGaussian.m0 Z5 k; m6 k4 l/ c
│ normalizeRatings.m6 d+ O5 N5 ]# L0 M& a U
│ selectThreshold.m' Q+ x. t; _( e2 o" A4 H
│ submit.m2 Z& L U5 m' I+ Y" j0 [/ E; ]
│ submitWeb.m# b* w9 t) c) q+ e% s! T5 T, l: |
│ visualizeFit.m
& G( R Q$ M1 Y3 C; }! e5 a8 }7 H │
}9 T1 w( Y% B9 B' ~% k ├─003_机器学习基石
) T) _# Y8 K5 V5 \ │ └─003_机器学习基石
- G( _& x. z$ c3 p1 X │ │ 下载说明.txt) Z$ I2 Y' k6 u" C& F( ]( H
│ │ 关注我们.png! B7 l0 O/ ~9 v& b/ r5 f1 z
│ │ 攻城狮论坛=网络技术+编程视频.url
, _; h' ~. o, A: Z' E5 G; x │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
1 C2 y' O/ M; f9 A0 I2 C │ │ T5 P* u: _8 M3 j
│ ├─Machine Leaning Foundations - TNU+ q4 n( ?- j$ E# Q
│ │ 01_handout.pdf
9 @3 O; W4 D) z │ │ 02_handout.pdf: H/ H# Z* b) I
│ │ 03_handout.pdf; T/ y" y+ g8 m. [# K* R: ~5 C
│ │ 04_handout.pdf
2 a# R8 }: C, r; p6 x) z │ │ 05_handout.pdf4 U- P* h7 H0 t d0 E
│ │ 06_handout.pdf! R m+ a. t2 B+ \+ h
│ │ 07_handout.pdf
! _: W6 g/ [) w8 n# F │ │ 08_handout.pdf# U, o2 p, |6 ?- x; s/ w& U: q
│ │ 09_handout.pdf4 Z8 N! ^ ~, V. W6 R8 z
│ │ 1 - 1 - Course Introduction (10-58).mp4
1 p$ k- A, b) p$ \' @! e4 F( \# u │ │ 1 - 2 - What is Machine Learning (18-28).mp4% m; j, K& L; K/ H
│ │ 1 - 3 - Applications of Machine Learning (18-56).mp46 P* d( W- Y0 O: g' s
│ │ 1 - 4 - Components of Machine Learning (11-45).mp4
$ X( h) {4 c: m; G+ [; { │ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4
0 i8 E1 N" W/ n1 H9 x │ │ 10 - 1 - Logistic Regression Problem (14-33).mp4" v. T# V* J' G$ \) z% N
│ │ 10 - 2 - Logistic Regression Error (15-58).mp4
! z% {) Q# l8 `+ @: H │ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4
) X+ @4 \# m' c. _. }3 j- ^( v( Q │ │ 10 - 4 - Gradient Descent (19-18).mp4' r/ P* r2 v# W d
│ │ 10_handout.pdf5 C; q% F) x0 n/ T6 [
│ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp40 W$ w7 |' q0 X: G: p% U7 F$ q% q
│ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4
1 Z E5 X% Q. D* [ │ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp46 M+ C5 T" j* h
│ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4
- p6 Y, l9 u6 D6 k7 _ │ │ 11_handout.pdf
$ I8 W0 ^0 p+ m' W# B4 M; u │ │ 12 - 1 - Quadratic Hypothesis (23-47).mp49 A0 e& g+ D" p+ E8 s* _
│ │ 12 - 2 - Nonlinear Transform (09-52).mp4. y; f' v6 W& A% A3 d/ o
│ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp49 `/ O) d! Y6 T# _5 Y1 S h) H1 L
│ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4
; q0 v+ t" U# [% E* q3 l │ │ 12_handout.pdf. J! q* n( @( X! ^) m
│ │ 13 - 1 - What is Overfitting- (10-45).mp4
9 h0 O' m' u! Y │ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4
, r" ^& L& E( O# x+ }2 ? │ │ 13 - 3 - Deterministic Noise (14-07).mp4) o& N7 q: t/ f" o' U6 Y
│ │ 13 - 4 - Dealing with Overfitting (10-49).mp4
/ e3 P8 f# s( C0 i1 H N │ │ 13_handout.pdf" S8 s/ {/ A1 Q, r. D1 O+ Q
│ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4* _% \" @* b4 Z
│ │ 14 - 2 - Weight Decay Regularization (24-08).mp4
, V# W, g& E' M. X# ^ │ │ 14 - 3 - Regularization and VC Theory (08-15).mp4
* E8 p& X/ a0 I& H5 `! E │ │ 14 - 4 - General Regularizers (13-28).mp4
* `& S# M" V P% c2 L6 ^. x │ │ 14_handout.pdf6 e% S7 x8 I; X3 @7 }, s
│ │ 15 - 1 - Model Selection Problem (16-00).mp45 l4 e% n' P9 D6 f. N) u0 \0 Z
│ │ 15 - 2 - Validation (13-24).mp41 I" {1 b! R4 I. H% r+ N; a+ c
│ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4
1 G- `* J* v0 x8 W h │ │ 15 - 4 - V-Fold Cross Validation (10-41).mp48 b$ W2 p# Y! c! T+ j
│ │ 15_handout.pdf& C/ q9 b" K7 z9 \) [0 G( r$ j1 r
│ │ 16 - 1 - Occam-'s Razor (10-08).mp4
. X8 F. |: h# Y6 a │ │ 16 - 2 - Sampling Bias (11-50).mp4$ _2 N { d( [6 ]+ P
│ │ 16 - 3 - Data Snooping (12-28).mp4
2 ]6 j4 S" S! W0 i │ │ 16 - 4 - Power of Three (08-49).mp46 u8 v' s2 z) s \) [
│ │ 16_handout.pdf
& U8 L L( a# C │ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4
2 r/ v, q: h/ n7 m o- U │ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4
( `$ p+ Z, U* b6 L6 L │ │ 2 - 3 - Guarantee of PLA (12-37).mp40 I& P4 P) ]% p* }9 D
│ │ 2 - 4 - Non-Separable Data (12-55).mp4
) m8 l# L5 B) Z8 j: X) L% _$ a* N [% t& F │ │ 3 - 1 - Learning with Different Output Space (17-26).mp45 O) K7 H) a" m# l2 `" [" ^
│ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
$ |' J, Q- }; ?+ P2 }0 M' m │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4
. G" U- D+ e6 ]- C' ] │ │ 3 - 4 - Learning with Different Input Space (14-13).mp4
- U( q. E/ q( F* h' t% j │ │ 4 - 1 - Learning is Impossible- (13-32).mp42 l$ P9 V5 J o( K3 N, E
│ │ 4 - 2 - Probability to the Rescue (11-33).mp4
B, I# a+ r* d/ d& i U. [ │ │ 4 - 3 - Connection to Learning (16-46).mp4
/ y. e6 C& x) n8 A │ │ 4 - 4 - Connection to Real Learning (18-06).mp4
2 N: N' G U, I6 H │ │ 5 - 1 - Recap and Preview (13-44).mp4+ i5 ?8 U$ j. n6 }
│ │ 5 - 2 - Effective Number of Lines (15-26).mp4
/ {1 E, M6 ?+ a7 t │ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp4
- a. z. }* S$ k │ │ 5 - 4 - Break Point (07-44).mp4
6 D. N! f0 J+ z! [% ~9 I │ │ 6 - 1 - Restriction of Break Point (14-18).mp4
* f) a/ z% t& l- j) q │ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4. k2 r7 q5 |; s. q1 v4 f
│ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
$ i0 B9 a- I$ u+ U │ │ 6 - 4 - A Pictorial Proof (16-01).mp4
8 W6 c5 I5 G# m │ │ 7 - 1 - Definition of VC Dimension (13-10).mp44 z @/ K$ N: t" N1 b
│ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp4
! ?! X2 D3 d' F6 E │ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4
$ F- O1 _' I2 O* z# p0 Q: ~ │ │ 7 - 4 - Interpreting VC Dimension (17-13).mp4
0 Q# e2 f) i( I$ P3 ]1 i9 f6 v │ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4
* I$ S! ^* w& j. S0 k" }# Y5 h. t │ │ 8 - 2 - Error Measure (15-10).mp4
6 X6 N# O" f& } \8 A! @/ t; L │ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4
- T3 u* ^5 f" l2 L │ │ 8 - 4 - Weighted Classification (16-54).mp4- f* x/ m8 V; S3 u* } P# N
│ │ 9 - 1 - Linear Regression Problem (10-08).mp4
9 n: k) l7 Z8 v0 E/ X: q& A │ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4, j' R0 T1 b+ e9 y* p
│ │ 9 - 3 - Generalization Issue (20-34).mp4
6 w$ v i% _) |: o/ [ Q │ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp4; E* S( g# u! {- u7 O; ?6 q
│ │
. }% m) }7 ]- R7 _( t) z1 S% O* V │ ├─机器学习的基石
, d8 A. b# f5 b8 I │ │ 1 - 1 - Course Introduction (10-58).mp4. z& i3 Q. K2 {* \' e' [
│ │ 1 - 2 - What is Machine Learning (18-28).mp41 B1 U$ k5 e7 v" Y
│ │ 1 - 3 - Applications of Machine Learning (18-56).mp4
; L3 E8 I) A p v1 p7 k │ │ 1 - 4 - Components of Machine Learning (11-45).mp4
5 [1 `5 y/ B: {5 U1 | │ │ 1 - 5 - Machine Learning and Other Fields (10-21).mp4
9 S' n+ V; N" E$ |3 K3 B. t/ P │ │ 10 - 1 - Logistic Regression Problem (14-33).mp4' O x- `1 i. o. ?4 b$ v. h
│ │ 10 - 2 - Logistic Regression Error (15-58).mp4
+ v* ^5 T8 e" K7 | │ │ 10 - 3 - Gradient of Logistic Regression Error (15-38).mp4
( X% G$ j J& J8 i+ C │ │ 10 - 4 - Gradient Descent (19-18).mp4
/ B5 L9 Z. Z8 e. ]% N% F+ ~: _- d │ │ 11 - 1 - Linear Models for Binary Classification (21-35).mp4; X f0 t9 V- F. _$ o3 S. c
│ │ 11 - 2 - Stochastic Gradient Descent (11-39).mp4
! e/ D3 E3 W7 L* n$ } │ │ 11 - 3 - Multiclass via Logistic Regression (14-18).mp4
+ j2 J. P" `5 S7 C8 ?* p# f │ │ 11 - 4 - Multiclass via Binary Classification (11-35).mp4
, D& C$ d" E# ~. S! ^% j1 d3 B) ~ │ │ 12 - 1 - Quadratic Hypothesis (23-47).mp42 A5 y4 m6 [, Q5 H1 y8 C4 _4 U* y: K
│ │ 12 - 2 - Nonlinear Transform (09-52).mp4
( o) k F, X, P. ^( q1 Q ~% L: b │ │ 12 - 3 - Price of Nonlinear Transform (15-37).mp4
/ w! {, y+ l' q Z W* Z1 p │ │ 12 - 4 - Structured Hypothesis Sets (09-36).mp4
9 e5 ~! @( N" [5 ^, D │ │ 13 - 1 - What is Overfitting- (10-45).mp4
6 v( f* |" A8 y* ~ │ │ 13 - 2 - The Role of Noise and Data Size (13-36).mp4
1 C# e2 _+ x9 q1 `) [3 x │ │ 13 - 3 - Deterministic Noise (14-07).mp4: b- a9 Y0 F9 K% }
│ │ 13 - 4 - Dealing with Overfitting (10-49).mp48 f8 q8 L0 u- P) p4 c9 u- D
│ │ 14 - 1 - Regularized Hypothesis Set (19-16).mp4
9 y* Z' l/ `. l! Y8 o9 u3 q │ │ 14 - 2 - Weight Decay Regularization (24-08).mp4
0 i5 u# @+ d: Y- A; U │ │ 14 - 3 - Regularization and VC Theory (08-15).mp4
5 ~: O: W8 n6 W& J/ h6 k │ │ 14 - 4 - General Regularizers (13-28).mp41 _ l, J2 Y0 k# d1 H# c
│ │ 15 - 1 - Model Selection Problem (16-00).mp4
; G1 W3 t" P6 M. {& G) v2 r1 d. K │ │ 15 - 2 - Validation (13-24).mp4
# L, Y) [8 J: o" i6 S6 U │ │ 15 - 3 - Leave-One-Out Cross Validation (16-06).mp4: |) X3 s) V- ^
│ │ 15 - 4 - V-Fold Cross Validation (10-41).mp4
+ @- N6 o$ D+ n │ │ 16 - 1 - Occam-'s Razor (10-08).mp4" w. x" v2 ~$ ]0 J$ C
│ │ 16 - 2 - Sampling Bias (11-50).mp42 c1 K+ o Y! g2 S& P
│ │ 16 - 3 - Data Snooping (12-28).mp4$ B; j- J8 i8 u, s: j
│ │ 16 - 4 - Power of Three (08-49).mp4* [- E* U( L0 e
│ │ 2 - 1 - Perceptron Hypothesis Set (15-42).mp4
% X7 {# f$ ]# X: B) M3 H │ │ 2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4
9 ]2 j! T/ n& R* J* h3 _ │ │ 2 - 3 - Guarantee of PLA (12-37).mp49 l2 Q: U }- w4 D4 ~3 J3 x
│ │ 2 - 4 - Non-Separable Data (12-55).mp4+ V9 _5 B* H; f/ k
│ │ 3 - 1 - Learning with Different Output Space (17-26).mp4
1 w1 P* L7 P9 `0 @; ` i, u │ │ 3 - 2 - Learning with Different Data Label (18-12).mp4
; Q- c3 M5 d/ c0 o& J" \( w │ │ 3 - 3 - Learning with Different Protocol (11-09).mp4( J6 d* L3 d0 L+ N
│ │ 3 - 4 - Learning with Different Input Space (14-13).mp4% h. L7 k0 [# T, G- I! x g
│ │ 4 - 1 - Learning is Impossible- (13-32).mp43 D9 ]. W, S) g
│ │ 4 - 2 - Probability to the Rescue (11-33).mp4
9 J% G* [) ?! s% c+ c! v+ L │ │ 4 - 3 - Connection to Learning (16-46).mp4
0 s( j4 X0 i+ }0 A* G, N, Q │ │ 4 - 4 - Connection to Real Learning (18-06).mp4
* ]* N' P2 T! _3 j* _. v │ │ 5 - 1 - Recap and Preview (13-44).mp4
3 Z* n, s! B) z* ^ │ │ 5 - 2 - Effective Number of Lines (15-26).mp4, {( E) H' } S! g% |6 T3 h2 S
│ │ 5 - 3 - Effective Number of Hypotheses (16-17).mp48 }& U$ ]+ B# {- d) H( L- @" R8 z
│ │ 5 - 4 - Break Point (07-44).mp44 W7 @8 @/ l: x& j4 b4 S6 K4 E4 J7 P
│ │ 6 - 1 - Restriction of Break Point (14-18).mp4
8 Z9 w9 |2 n% Z5 l- e7 j │ │ 6 - 2 - Bounding Function- Basic Cases (06-56).mp4+ Q5 h3 h6 x% _# y0 s, a9 H# P
│ │ 6 - 3 - Bounding Function- Inductive Cases (14-47).mp4
" i3 W6 v4 g [0 d/ J% S/ n │ │ 6 - 4 - A Pictorial Proof (16-01).mp4# j M2 s* o7 n4 t( h
│ │ 7 - 1 - Definition of VC Dimension (13-10).mp4; \7 @% w5 M" K0 ~
│ │ 7 - 2 - VC Dimension of Perceptrons (13-27).mp4& f8 k; ^) P4 c6 ?/ T' L6 L
│ │ 7 - 3 - Physical Intuition of VC Dimension (6-11).mp4, x2 T; b" N* U! M; o/ t1 m
│ │ 7 - 4 - Interpreting VC Dimension (17-13).mp4
9 p& G' e- R' |+ ?! P) p5 |- _5 s │ │ 8 - 1 - Noise and Probabilistic Target (17-01).mp4: p& Y, F* e' T) m& Q$ I4 {4 |
│ │ 8 - 2 - Error Measure (15-10).mp4" R: W/ N- h# I! p
│ │ 8 - 3 - Algorithmic Error Measure (13-46).mp4
) s0 x8 g6 F3 }+ o' Z% z: y │ │ 8 - 4 - Weighted Classification (16-54).mp4( H" T) a/ f U
│ │ 9 - 1 - Linear Regression Problem (10-08).mp4
% S9 |8 [( J' ]3 i7 G/ |" x s │ │ 9 - 2 - Linear Regression Algorithm (20-03).mp4
2 n5 K. R. K6 M5 M* y/ e4 \) ` │ │ 9 - 3 - Generalization Issue (20-34).mp4! G% k; Z& l! r( t$ D- Q
│ │ 9 - 4 - Linear Regression for Binary Classification (11-23).mp4& ~5 j0 S! t$ K J5 V" E0 @
│ │ lecture_slides-01_handout.pdf! A& D3 ?1 Q1 k0 n9 U7 u `
│ │ lecture_slides-02_handout.pdf
* _) z- }7 i9 _ │ │ lecture_slides-03_handout.pdf5 j1 r$ ^% Q+ @. V) f; ?
│ │ lecture_slides-04_handout.pdf ^. C9 s* l. |$ D
│ │ lecture_slides-05_handout.pdf
% k8 O# m H+ @! c3 o │ │ lecture_slides-06_handout.pdf
! c7 ^$ P5 D; I, B │ │ lecture_slides-07_handout.pdf; O2 K9 Z# b& Q- o( H6 x
│ │ lecture_slides-08_handout.pdf _" N* _' X( Z+ F
│ │ lecture_slides-09_handout.pdf! x9 S. I# e" r3 v0 j
│ │ lecture_slides-10_handout.pdf; f3 c% d4 B& W4 A8 X2 i
│ │ lecture_slides-11_handout.pdf: _9 t8 ?5 n; Z1 ?
│ │ lecture_slides-12_handout.pdf9 i( }% U" ]0 ]; k# J8 a* y$ z+ k, j I
│ │ lecture_slides-13_handout.pdf' E; _/ S( R6 {" ~) S. a
│ │ lecture_slides-14_handout.pdf: O" N2 e, w; ^3 W. X' C
│ │ lecture_slides-15_handout.pdf/ {9 S2 F' V0 z/ N& P! m
│ │ lecture_slides-16_handout.pdf
- _! {/ Y+ h4 G" W0 H0 f, N │ │ ) d" C4 W2 S4 r9 k) m1 C
│ └─機器學習基石
2 G8 n% B: \1 R A9 s: |" ] │ ├─01_-_The_Learning_Problem
7 a/ D& f/ V2 ^% n4 e! U │ │ 01_Course_Introduction_10-58.mp4* [# k B3 ]# k ]; ]7 v: h$ k5 E" U
│ │ 01_Course_Introduction_10-58.pdf
% E& f; L# N( F │ │ 02_What_is_Machine_Learning_18-28.mp4$ f7 w; G1 E2 [5 _
│ │ 03_Applications_of_Machine_Learning_18-56.mp4
/ ?6 w! t& A5 R9 d, Z │ │ 04_Components_of_Machine_Learning_11-45.mp4
" E2 @5 m9 W0 E. s' h6 j │ │ 05_Machine_Learning_and_Other_Fields_10-21.mp4
( _9 e* S8 Y. W) P" { │ │ # @! B" Q+ _0 w$ T5 K$ @% K
│ ├─02_-_Learning_to_Answer_Yes-No
: F6 u! d% L+ K e4 A │ │ 01_Perceptron_Hypothesis_Set_15-42.mp44 @9 x: B/ Q" o9 p
│ │ 01_Perceptron_Hypothesis_Set_15-42.pdf
2 O8 M+ E2 ~! h8 J% F7 N │ │ 02_Perceptron_Learning_Algorithm_PLA_19-46.mp4
, _1 K; Z! c) r9 @" r │ │ 03_Guarantee_of_PLA_12-37.mp4+ T, D( L, G4 D( ]% T! i
│ │ 04_Non-Separable_Data_12-55.mp4
6 Q5 E2 T# u; ^ g' y( Q5 U7 G │ │ 0 B6 o7 S7 ~& G; b8 B! _
│ ├─03_-_Types_of_Learning3 l, s0 K9 L' c" J
│ │ 01_Learning_with_Different_Output_Space_17-26.mp4
0 ?2 z- l8 p3 u+ x( K+ M" ]1 i │ │ 01_Learning_with_Different_Output_Space_17-26.pdf4 S5 Y$ q4 p0 ~6 y" s# h5 n# _
│ │ 02_Learning_with_Different_Data_Label_18-12.mp4: p# \. E8 n6 i2 ]
│ │ 03_Learning_with_Different_Protocol_11-09.mp4
) e- M0 a2 M e) J/ a │ │ 04_Learning_with_Different_Input_Space_14-13.mp4: \5 Q7 t8 |/ ` j4 ^
│ │
% e' P' }: `6 h. u │ ├─04_-_Feasibility_of_Learning4 y$ P$ j/ i! F& U) @# G: t
│ │ 01_Learning_is_Impossible_13-32.mp45 Q9 t9 P/ d, y! [0 ~+ `
│ │ 01_Learning_is_Impossible_13-32.pdf1 R* h$ T7 I( P2 _. Y/ a# \6 a' }
│ │ 02_Probability_to_the_Rescue_11-33.mp4
0 [8 c) U1 B2 x, [$ W5 G! A │ │ 03_Connection_to_Learning_16-46.mp4; W: t8 F8 T3 E7 j) r5 ?
│ │ 04_Connection_to_Real_Learning_18-06.mp4) u6 `# h ?+ \% N# G1 X* D8 m4 u
│ │
* e! e d, |3 U │ ├─05_-_Training_versus_Testing# L! c x% U$ i) s
│ │ 01_Recap_and_Preview_13-44.mp42 k! X+ _$ v/ l% v
│ │ 01_Recap_and_Preview_13-44.pdf+ C1 i) Q* s% N, }4 B! N
│ │ 02_Effective_Number_of_Lines_15-26.mp4. Y3 r. k+ V: B! K; }
│ │ 03_Effective_Number_of_Hypotheses_16-17.mp4* x4 D6 \- m5 B
│ │ 04_Break_Point_07-44.mp4
# a+ Z8 ]" I& `1 Z │ │ 3 A3 u n2 c6 A4 o4 x
│ ├─06_-_Theory_of_Generalization
" Y/ g! n& K6 W6 Q4 P │ │ 01_Restriction_of_Break_Point_14-18.mp40 g$ w7 d; T- J3 N! |/ {
│ │ 01_Restriction_of_Break_Point_14-18.pdf
! k4 X% m/ k0 z r) E Z │ │ 02_Bounding_Function-_Basic_Cases_06-56.mp4 u! Y" [# p+ Q {7 U
│ │ 03_Bounding_Function-_Inductive_Cases_14-47.mp4& ~/ L, N* L6 z" T6 ^- e9 }
│ │ 04_A_Pictorial_Proof_16-01.mp4
0 @" `* l+ U8 \# t │ │
' M* ?& j. n5 w. {1 X │ ├─07_-_The_VC_Dimension
# s& i. b2 A% f- G6 A1 g# b% d, D5 m' N │ │ 01_Definition_of_VC_Dimension_13-10.mp4/ Y6 G: T- U6 }
│ │ 01_Definition_of_VC_Dimension_13-10.pdf; Z3 p1 t8 ]6 ?' D' P8 ]) O
│ │ 02_VC_Dimension_of_Perceptrons_13-27.mp4
3 d3 @$ Z- Q) \8 Y0 H7 h │ │ 03_Physical_Intuition_of_VC_Dimension_6-11.mp47 ?% A i; S5 O) f1 R% z: z* T
│ │ 04_Interpreting_VC_Dimension_17-13.mp4# N* o8 M `( D/ E) c
│ │
+ N6 v+ [' y' j6 y3 V9 S2 c0 V. m% A% z │ ├─08_-_Noise_and_Error
. u4 a$ v# ~1 l C( Z7 C% O │ │ 01_Noise_and_Probabilistic_Target_17-01.mp4
$ G) V$ b7 `/ {8 ~; ]3 [ │ │ 01_Noise_and_Probabilistic_Target_17-01.pdf+ J6 A* r4 C1 o, p) n6 V- n2 Q! q
│ │ 02_Error_Measure_15-10.mp4
+ M8 E% Z0 r( b- W, f& e/ | │ │ 03_Algorithmic_Error_Measure_13-46.mp4
& b8 T* Q+ O: ]) w │ │ 04_Weighted_Classification_16-54.mp4. k+ x9 a$ m' }6 r
│ │ & `/ v; x/ m2 z# e( v/ L* D7 _2 N
│ ├─09_-_Linear_Regression, y& R5 ~+ a9 n0 k* `) R# U
│ │ 01_Linear_Regression_Problem_10-08.mp4
! \6 G' d3 K g0 S( Y │ │ 01_Linear_Regression_Problem_10-08.pdf
O. p1 u9 v1 D; ]. j │ │ 02_Linear_Regression_Algorithm_20-03.mp4
( y7 x% i1 Q5 O: N M, ] │ │ 03_Generalization_Issue_20-34.mp4
4 }, S, @0 Q0 Z: G$ ^9 ` │ │ 04_Linear_Regression_for_Binary_Classification_11-23.mp4
& g: Y+ d' ?5 n' X- ^( V2 H │ │
" V+ O4 Y$ @2 {, H │ ├─10_-_Logistic_Regression
0 M: l( r* R1 d/ D. N1 `+ u( W │ │ 01_Logistic_Regression_Problem_14-33.mp4
c B$ p7 Z: z; D0 L │ │ 01_Logistic_Regression_Problem_14-33.pdf5 J) W, k: N- q9 a1 x# D
│ │ 02_Logistic_Regression_Error_15-58.mp4
# T ~, \0 H1 ^9 b1 z* ? │ │ 03_Gradient_of_Logistic_Regression_Error_15-38.mp4
+ N9 G# ^5 Z& J/ i! t& q& B! Y │ │ 04_Gradient_Descent_19-18.mp4+ R3 ~! P; x% `% r( e
│ │ + ~: b9 z& Z& Y7 X4 ~& Q
│ ├─11_-_Linear_Models_for_Classification( F$ k: Y0 c2 o+ Y" ~
│ │ 01_Linear_Models_for_Binary_Classification_21-35.mp45 I1 U7 f- a; N; a: K
│ │ 01_Linear_Models_for_Binary_Classification_21-35.pdf0 B$ c7 k. E6 ~: q1 [
│ │ 02_Stochastic_Gradient_Descent_11-39.mp4
7 O) w4 q# d- k' n7 A [6 W( p" N6 V: P │ │ 03_Multiclass_via_Logistic_Regression_14-18.mp4
# ?6 X( P5 u s% f │ │ 04_Multiclass_via_Binary_Classification_11-35.mp4% s9 P8 b9 m' }' v
│ │
: d' [- M# F. A! b. B- P# f" \$ C5 D, I │ ├─12_-_Nonlinear_Transformation
+ _- T* f6 R' B) Y, q1 t │ │ 01_Quadratic_Hypothesis_23-47.mp4
5 ?) p% H8 X# G" q+ w( r │ │ 01_Quadratic_Hypothesis_23-47.pdf: s1 t; u, F1 m, a8 R. t; O' L3 m: F
│ │ 02_Nonlinear_Transform_09-52.mp4
( K3 Z0 w; E% r* @6 F# z │ │ 03_Price_of_Nonlinear_Transform_15-37.mp4( l7 z# {* r; k. N8 h3 ]
│ │ 04_Structured_Hypothesis_Sets_09-36.mp4
% r# Y- V$ f* B │ │
# @" S2 F" o$ \6 {5 H) j" Y0 O │ ├─13_-_Hazard_of_Overfitting
, l7 P L# k1 o% T* |( X │ │ 01_What_is_Overfitting_10-45.mp4/ }" ?5 z- P) m# w
│ │ 01_What_is_Overfitting_10-45.pdf
$ }9 K$ |6 ?' `; S% z# { │ │ 02_The_Role_of_Noise_and_Data_Size_13-36.mp46 s) @ P) U1 F- W' q2 R
│ │ 03_Deterministic_Noise_14-07.mp4$ M4 b) {' m0 f" O
│ │ 04_Dealing_with_Overfitting_10-49.mp4& j0 c1 f; y. R9 m; f! S& b; O2 p
│ │
( p4 c# F* P; k. E3 X │ ├─14_-_Regularization
. T2 s0 ?4 V0 K │ │ 01_Regularized_Hypothesis_Set_19-16.mp4
( m: V9 b( n& a- s% Q8 i0 p │ │ 01_Regularized_Hypothesis_Set_19-16.pdf
* y1 \: q8 l. F7 a* X3 Q │ │ 02_Weight_Decay_Regularization_24-08.mp4
3 G5 h5 t" n9 q2 r; q' P, O9 @4 v │ │ 03_Regularization_and_VC_Theory_08-15.mp4# o$ m0 A1 j3 o
│ │ 04_General_Regularizers_13-28.mp42 K6 m% x, o: W D+ O6 W' r
│ │
9 V8 H* u( ~2 S n5 x j │ ├─15_-_Validation/ |3 Q, U) `$ ?2 X
│ │ 01_Model_Selection_Problem_16-00(437).mp4
* i5 H# p* a5 a5 p │ │ 01_Model_Selection_Problem_16-00.pdf
! T5 W4 Z8 w/ y# V; O │ │ 02_Validation_13-24.mp4+ n2 ^6 w' S2 R' g! b( k
│ │ 03_Leave-One-Out_Cross_Validation_16-06.mp4( l) i2 `& s$ P
│ │ 04_V-Fold_Cross_Validation_10-41.mp4
3 u* h# c6 R5 d; e" ~9 ?: v │ │ 4 c# y; F- \* X
│ └─16_-_Three_Learning_Principles
# s7 O* H, S: p! g) d- u │ 01_Occams_Razor_10-08.mp4- ^- _! Q: |1 V% C( }& k7 ]6 A
│ 01_Occams_Razor_10-08.pdf
! D$ W6 n0 x" R$ i) H( e │ 02_Sampling_Bias_11-50.mp47 q1 ~# ~6 d. s0 s+ k+ X3 h( Z8 Z, n
│ 03_Data_Snooping_12-28.mp40 n0 q) \1 t* w5 J& ]+ |' J8 j8 d
│ 04_Power_of_Three_08-49.mp4
( F) n9 F$ A0 B/ n/ T │ 2 ~& k- ]' o" v$ P
├─004_机器学习技法
9 ?$ V7 C0 g) M1 x/ F5 F& Y │ └─004_机器学习技法
' Q3 \: Q6 i2 A. a) X) q3 `5 Q │ └─機器學習技法
* l( e0 k5 ^( o8 l0 m3 J │ │ 下载说明.txt
$ c! E' @5 g: ]0 _6 [ │ │ 关注我们.png6 J* T q9 a1 X6 m
│ │ 攻城狮论坛=网络技术+编程视频.url
. }- [6 h8 s3 t7 a6 _3 A │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
5 g- @6 z: c$ C2 J │ │ / P* ^/ U' J9 K& [# S v) m
│ ├─01_Linear_Support_Vector_Machine: v7 L2 K' T8 y7 a# H: K4 t
│ │ 01_Course_Introduction_4-07.mp4
* w7 [1 L, Z8 ^4 U& y8 E$ i │ │ 01_Course_Introduction_4-07.pdf
+ C8 w+ ~) S) S: o6 l, ?9 h- b6 t( _ │ │ 02_Large-Margin_Separating_Hyperplane_14-17.mp4
. L3 K1 j4 Y) j. a; Y8 e8 o │ │ 03_Standard_Large-Margin_Problem_19-16.mp4
" N; B% S3 M. W8 v( J7 u │ │ 04_Support_Vector_Machine_15-33.mp4" i/ g9 R) b P) g
│ │ 05_Reasons_behind_Large-Margin_Hyperplane_13-31.mp40 i3 ~& X! [9 I) U4 v- c
│ │ 2 H( R: z2 m4 Q1 P# N
│ ├─02_Dual_Support_Vector_Machine
0 ^9 B# Y0 d( I& N6 H0 ^& K │ │ 01_Motivation_of_Dual_SVM_15-54.mp48 _$ F0 h# Z2 p) w) j1 r
│ │ 01_Motivation_of_Dual_SVM_15-54.pdf
2 f7 D# n/ n1 W3 h7 w │ │ 02_Lagrange_Dual_SVM_18-50.mp4
9 R! u- D# t9 @4 k5 h) u* v │ │ 03_Solving_Dual_SVM_14-19.mp4* ^5 y0 D3 @& x+ w! V9 M8 `6 \3 X9 t
│ │ 04_Messages_behind_Dual_SVM_11-18.mp4 n) Q- h$ ]6 j1 a$ k' S
│ │
! V; b% c; r) f/ c: f) v) F │ ├─03_Kernel_Support_Vector_Machine
: s5 e h% y0 J( | │ │ 01_Kernel_Trick_20-23.mp4! T" C* x8 j0 V- {" p. D1 [' g
│ │ 01_Kernel_Trick_20-23.pdf& }. Y2 A& I+ _
│ │ 02_Polynomial_Kernel_12-16.mp4 y8 l4 c: w( l
│ │ 03_Gaussian_Kernel_14-43.mp4
& S P! c. p4 ^; q3 t/ M │ │ 04_Comparison_of_Kernels_13-35.mp4& _% e' X4 H# A# i
│ │
# J: _ P) I1 l. M │ ├─04_Soft-Margin_Support_Vector_Machine
1 |" H/ H6 c% q │ │ 01_Motivation_and_Primal_Problem_14-27.mp4# `& G" v* J$ U( l
│ │ 01_Motivation_and_Primal_Problem_14-27.pdf; L* J- B. J! S% Q1 D
│ │ 02_Dual_Problem_7-38.mp4
0 @$ X2 _! {7 H" H! K" }7 @ │ │ 03_Messages_behind_Soft-Margin_SVM_13-44.mp4
9 q8 m7 O. |: A- h# t3 B) Y │ │ 04_Model_Selection_9-57.mp48 a: p9 K2 Y0 C: t& ] U0 x$ D/ |
│ │
0 O6 q+ N6 r& Y$ @, V │ ├─05_Kernel_Logistic_Regression
e4 ], v: l/ T │ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.mp4/ _1 A7 A \0 L& c
│ │ 01_Soft-Margin_SVM_as_Regularized_Model_13-40.pdf
6 ^% o G' ]( \- N5 R; A │ │ 02_SVM_versus_Logistic_Regression_10-18.mp4
! v5 a( e; N* T+ s. N │ │ 03_SVM_for_Soft_Binary_Classification_9-36.mp4
0 C( s' B; A( H A' w" K- ?$ W# l │ │ 04_Kernel_Logistic_Regression_16-22.mp4
$ Z- h* g# j2 u' ^8 Q) k* u │ │ 7 k1 t( v7 d- G7 _
│ ├─06_Support_Vector_Regression9 U, |2 f/ t- `( l: H- N6 m) |: Z: g
│ │ 01_Kernel_Ridge_Regression_17-17.mp45 S, |% t5 j \: y9 ?! g; z
│ │ 01_Kernel_Ridge_Regression_17-17.pdf
. ~% Y( Z# O) P │ │ 02_Support_Vector_Regression_Primal_18-44.mp4, e4 E8 w% @ P( n5 _7 Z
│ │ 03_Support_Vector_Regression_Dual_13-05.mp47 p2 e/ A U+ w2 \* n) j8 T" z ^
│ │ 04_Summary_of_Kernel_Models_09-06.mp43 ^ g+ A( B3 q* k# \6 L$ ~+ z$ }
│ │ : S k) n& G( J7 _# Y, N1 }
│ ├─07_Blending_and_Bagging; g& y' F# Q% d7 K
│ │ 01_Motivation_of_Aggregation_18-54.mp4
' r8 c$ |" Q G. B! p* h │ │ 01_Motivation_of_Aggregation_18-54.pdf# N) t( T2 A1 G1 y! F% _2 T- m
│ │ 02_Uniform_Blending_20-31.mp4
5 c8 a! @, T2 O* t1 ` │ │ 03_Linear_and_Any_Blending_16-48.mp4$ L" H, P; |- E9 T
│ │ 04_Bagging_Bootstrap_Aggregation_11-48.mp4
* g5 X$ y8 ]! q │ │
0 V5 k; k% S. v3 S1 R3 p │ ├─08_Adaptive_Boosting0 x% l4 ~4 c" N3 t
│ │ 01_Motivation_of_Boosting_12-47.mp4 s" h. H* j/ o4 r4 L, y2 V% x
│ │ 01_Motivation_of_Boosting_12-47.pdf
" A0 R u/ r7 Q4 T: x" m │ │ 02_Diversity_by_Re-weighting_14-28.mp4
! X* s x6 |' \9 I5 b' m │ │ 03_Adaptive_Boosting_Algorithm_13-34.mp4
1 s1 n: z1 W6 Q+ L │ │ 04_Adaptive_Boosting_in_Action_11-04.mp4, n. s4 ]* q1 u N0 i/ C
│ │
& T4 D# v( O% t │ ├─09_Decision_Tree
5 B1 b; d. P% t; F │ │ 01_Decision_Tree_Hypothesis_17-28.mp4
6 x: F4 t8 ?7 x% P │ │ 01_Decision_Tree_Hypothesis_17-28.pdf( ^+ `1 K' n) }' v) F, R3 @$ Y
│ │ 02_Decision_Tree_Algorithm_15-20.mp4/ w6 \2 k. M$ ]3 F7 `0 S2 ~, E& w
│ │ 03_Decision_Tree_Heuristics_in_CRT_13-21.mp4# ^, \) v* |5 e. F+ {1 _, x3 r
│ │ 04_Decision_Tree_in_Action_8-44.mp4" v0 {$ w9 {* k' x% d6 s) Q
│ │ * @0 I5 a/ v2 Q- W$ J$ {' [0 N+ x
│ ├─10_Random_Forest9 `) d. J5 w* [4 \( `, w
│ │ 01_Random_Forest_Algorithm_13-06.mp4
/ l8 z! I: q8 R │ │ 01_Random_Forest_Algorithm_13-06.pdf
% ~! G! A+ ?# D3 o2 K) }$ [ │ │ 02_Out-Of-Bag_Estimate_12-31.mp41 }% Q X& L; \8 c5 x5 f# t
│ │ 03_Feature_Selection_19-27.mp4
# z9 T. K, _; j: ]. t │ │ 04_Random_Forest_in_Action13-28.mp4
* v. }; |, _) Q/ h9 }; ]; Z │ │
" F& S) Y6 ~. f1 D5 C2 r- E( } n │ ├─11_Gradient_Boosted_Decision_Tree
' ?( N1 f$ \* H; w( G │ │ 01_Adaptive_Boosted_Decision_Tree_15-05.mp4
: Z0 m/ l9 |( L │ │ 01_Adaptive_Boosted_Decision_Tree_15-05.pdf
9 n: _3 C4 A6 e/ n* e1 [6 ^/ @% c │ │ 02_Optimization_View_of_AdaBoost_27-25.mp4
* A! N8 ~* L6 `3 C T; z9 k │ │ 03_Gradient_Boosting_18-20.mp4
# C! t- ~, I2 R9 h │ │ 04_Summary_of_Aggregation_Models_11-19.mp4
& r( n7 i; s, Q │ │ + q! E' d7 ]/ _' H2 s
│ ├─12_Neural_Network
- w" u2 H: E+ } │ │ 01_Motivation_20-36.mp48 v+ O! V1 t$ d0 ?0 }9 u% }9 J2 F
│ │ 01_Motivation_20-36.pdf8 U; A% C" A$ x' J! v9 A' u- [
│ │ 02_Neural_Network_Hypothesis_18-01.mp4
w" @! p, f3 ^( y- I- v+ v/ L │ │ 03_Neural_Network_Learning_22-26.mp4
" \/ S p3 n; d. @, P3 S! A │ │ 04_Optimization_and_Regularization_17-29.mp4
3 w5 d6 d9 N6 J0 L# @ │ │ ! D+ N# N/ T! ?9 L* x
│ ├─13_Deep_Learning
# Y5 F T) n- }" {% _# |6 Q │ │ 01_Deep_Neural_Network_21-30.mp4
7 I, p0 `; k9 H- i# Z5 t- @0 M$ x │ │ 01_Deep_Neural_Network_21-30.pdf
6 I) J: O: s+ r& Q1 a+ P, {( j │ │ 02_Autoencoder_15-17.mp42 p {; N v/ [3 H y
│ │ 03_Denoising_Autoencoder_8-30.mp4
: G' _% h* {0 N0 |. U3 W9 d [ │ │ 04_Principal_Component_Analysis_31-20.mp43 r" I! L q8 R$ }* X5 {; h9 E
│ │ + m& v C3 f+ d! s
│ ├─14_Radial_Basis_Function_Network
5 m. Y a: s: e7 `) f │ │ 01_RBF_Network_Hypothesis_12-55.mp4. @2 w. {* T4 F: i# P3 ]. V$ @
│ │ 01_RBF_Network_Hypothesis_12-55.pdf
y: M6 s3 j. c$ h$ C2 b, t* s │ │ 02_RBF_Network_Learning_20-08.mp4
2 ]' n, Z/ T D │ │ 03_k-Means_Algorithm_16-19.mp4
7 m! v* d$ m n │ │ 04_k-Means_and_RBF_Network_in_Action_9-46.mp4; {0 k4 d3 x# S, ]% U
│ │ 1 S0 u" k7 j$ \+ @# `% W0 d: B
│ ├─15_Matrix_Factorization
$ x6 Z5 H2 `' }; K, {4 P │ │ 01_Linear_Network_Hypothesis_20-16.mp4
% a, H* f$ f; Y6 a: R" y$ U4 Q │ │ 01_Linear_Network_Hypothesis_20-16.pdf9 G1 W& q$ A& b; z- i) s# l
│ │ 02_Basic_Matrix_Factorization_16-32.mp4: K1 e7 f: {3 l' C9 B8 }
│ │ 03_Stochastic_Gradient_Descent_12-22.mp4- P3 S; g4 Q/ H0 C7 \' H) [4 y
│ │ 04_Summary_of_Extraction_Models_9-12.mp4: c6 j. {7 r4 s6 D
│ │
% M X: z8 p# e3 G% f │ └─16_Finale% I: f1 X5 ~, o( j- m4 V
│ 01_Feature_Exploitation_Techniques_16-11.mp4( Q$ @8 B. @# k" h- Q; v& A9 C4 K
│ 01_Feature_Exploitation_Techniques_16-11.pdf! D5 N- A' Z4 j: v$ G, ]
│ 02_Error_Optimization_Techniques_8-40.mp4
- d2 r0 H; O' N- G$ Y3 s │ 03_Overfitting_Elimination_Techniques_6-44.mp4
9 g% A. q+ T5 P5 k; v' u0 O2 {3 J │ 04_Machine_Learning_in_Action_12-59.mp4
3 F U' S- P9 n │ # b3 n1 \% [* G, f8 H8 ?( z/ g) ~- J2 n
├─005_Neural Networks for Machine Learning
, r- U. I) D/ |% K │ └─005_Neural Networks for Machine Learning
5 m; _$ F: B/ e, v │ │ 下载说明.txt
/ S# M% n3 N" d3 e6 d0 K7 s7 K- U │ │ 关注我们.png
* M4 [4 K6 t- l │ │ 攻城狮论坛=网络技术+编程视频.url
E* Y; K* y5 t1 O │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt: O4 W' m, I* g# ^0 D" @9 m, _/ T
│ │
3 o+ T# K& K* ~. E │ ├─hinton-ml% a! ^' B9 Q1 s1 f7 U
│ │ 1.Why do we need machine learning
- g. T' e: K; S/ c. J% O- G │ │ 1.Why do we need machine learning.mp4
1 A. ~" A8 n& N& ?2 f │ │ 10.What perceptrons can't do [15 min].mp4# A* G2 f1 S2 U% b8 Q
│ │ 10.What perceptrons can't do [15 min].srt0 w, |' P, u0 @/ ^5 C
│ │ 11.Learning the weights of a linear neuron [12 min].mp4, u$ o9 @+ ~% g3 `' l. K R2 Q0 J
│ │ 11.Learning the weights of a linear neuron [12 min].srt
/ B+ w! C, `7 F) D# H, t/ m │ │ 12.The error surface for a linear neuron [5 min].mp4/ Q# u) E- n* K( w+ w4 y
│ │ 12.The error surface for a linear neuron [5 min].srt
4 p6 i# }6 }6 A& `. |2 x+ L/ ^ │ │ 13.Learning the weights of a logistic output neuron [4 min].mp4) F5 O4 p$ l: B- ^0 H# A
│ │ 13.Learning the weights of a logistic output neuron [4 min].srt" I' n, l3 c, b% n
│ │ 14.The backpropagation algorithm [12 min].mp4
5 v9 }' n2 a5 ]' ~) X) D0 {+ @ │ │ 14.The backpropagation algorithm [12 min].srt
; S; X/ E" R8 T# ^ │ │ 15.Using the derivatives computed by backpropagation [10 min].mp4
% f. g8 Y6 Q5 ~: M4 g; y- J │ │ 15.Using the derivatives computed by backpropagation [10 min].srt: B" D, ^4 T4 N4 `
│ │ 16.Learning to predict the next word [13 min].mp4
' z# L2 b9 `4 K3 o/ ?" w. c8 D6 ] │ │ 16.Learning to predict the next word [13 min].srt% N/ G$ X0 k5 k _
│ │ 17.A brief diversion into cognitive science [4 min].mp4
! Z0 }0 ]2 T- h" E1 l) E │ │ 17.A brief diversion into cognitive science [4 min].srt* k) j+ G0 N( h) `0 T0 i$ |
│ │ 19.Neuro-probabilistic language models [8 min].mp4
, }+ Q) A# Y( F* ?* v6 { │ │ 19.Neuro-probabilistic language models [8 min].srt& c( ^6 d, R! D) U
│ │ 2.What are neural networks" {- m4 v9 `+ d) v9 }
│ │ 2.What are neural networks.mp4, }( v: Z+ A& X6 P4 M
│ │ 20.Ways to deal with the large number of possible outputs [15 min].mp46 j3 Y) Z% |( c# F
│ │ 20.Ways to deal with the large number of possible outputs [15 min].srt
- O6 z, e% R7 H5 ?( h1 p │ │ 21.Why object recognition is difficult [5 min].mp4
% Z7 c; E3 J" K* ~1 f │ │ 21.Why object recognition is difficult [5 min].srt4 e( Q$ _% h. @ U3 i7 ?* W( Y
│ │ 22.Achieving viewpoint invariance [6 min].mp48 Q* q6 C2 ]' `# s! \
│ │ 22.Achieving viewpoint invariance [6 min].srt
3 G1 {* R( W" K( P, D3 z; D │ │ 23.Convolutional nets for digit recognition [16 min].mp4' E7 `5 Y+ V- z1 ^) Y
│ │ 23.Convolutional nets for digit recognition [16 min].srt
: x0 G0 W/ O2 m6 l" Z │ │ 24.Convolutional nets for object recognition [17min].mp4
; x# e* w1 m0 l │ │ 24.Convolutional nets for object recognition [17min].srt
$ L9 P! U! k) P │ │ 25.Overview of mini-batch gradient descent.mp44 k F/ H+ M& r1 ^) v9 x! C* }0 t& @
│ │ 25.Overview of mini-batch gradient descent.srt6 {+ S# S# _0 Y: I4 d
│ │ 26.A bag of tricks for mini-batch gradient descent.mp47 D3 T+ w4 x0 S
│ │ 26.A bag of tricks for mini-batch gradient descent.srt
! S- F1 R5 A* T% K) T: l │ │ 27.The momentum method.mp4
: \6 r/ O$ y/ _: P. _( U3 ] │ │ 27.The momentum method.srt* z3 R6 Z" |+ o! j9 N' T. ^
│ │ 28.Adaptive learning rates for each connection.mp4+ t0 U. A" U9 X( m
│ │ 28.Adaptive learning rates for each connection.srt
+ X% e) D8 D4 |: f% q3 x │ │ 3.Some simple models of neurons [8 min].mp4
* t1 ^& u9 U/ J' J │ │ 3.Some simple models of neurons [8 min].srt
/ p- [: I! R; Z" G0 P │ │ 31.Training RNNs with back propagation.mp4
6 h4 G. \2 B& z& N) n% ] _ │ │ 31.Training RNNs with back propagation.srt3 a: |3 w: N. Y6 M
│ │ 32.A toy example of training an RNN.mp4- @" S' m" T% m+ f+ B: j
│ │ 32.A toy example of training an RNN.srt
% r3 p) {" T: K9 Z │ │ 33.Why it is difficult to train an RNN.mp4
: A% i) G" {5 i v. G2 a. W$ Z │ │ 33.Why it is difficult to train an RNN.srt
6 ^) p- A& T* r n2 q │ │ 34.Long-term Short-term-memory.mp4. c% @& _8 e3 }8 } z' _+ o4 g
│ │ 34.Long-term Short-term-memory.srt, T- P8 |( L6 H& z; t; d
│ │ 35.A brief overview of Hessian Free optimization.mp4
3 H; k( H9 N) U/ w$ A) c │ │ 35.A brief overview of Hessian Free optimization.srt/ q i% ]1 ^( R
│ │ 37.Learning to predict the next character using HF [12 mins].mp45 Z' l% Q9 `. ?8 h6 f
│ │ 37.Learning to predict the next character using HF [12 mins].srt! ?; f1 i3 Y L6 n
│ │ 38.Echo State Networks [9 min].mp4
. N3 M. a* h6 N$ G │ │ 38.Echo State Networks [9 min].srt, `4 B! K" N0 m
│ │ 39.Overview of ways to improve generalization [12 min].mp4
8 C: D6 q( p) \( k- S6 U │ │ 39.Overview of ways to improve generalization [12 min].srt
0 ^) M# ?; U4 [# ^6 p" {* U! f7 Y │ │ 4.A simple example of learning [6 min].mp4
7 D; A6 q: j6 A, ?* O% V │ │ 4.A simple example of learning [6 min].srt( t5 Y7 R3 V( c Q. l5 H# x6 T
│ │ 40.Limiting the size of the weights [6 min].mp4
: U0 v: U. p7 x: b8 Y │ │ 40.Limiting the size of the weights [6 min].srt
7 Z& [/ G6 F4 r1 ^, \ │ │ 41.Using noise as a regularizer [7 min].mp4
B# X9 Z! y! T' E; m9 D │ │ 41.Using noise as a regularizer [7 min].srt+ R) u L$ R3 A# j' g6 [
│ │ 42.Introduction to the full Bayesian approach [12 min].mp4
3 H" [ S2 P4 ?7 t9 D) m8 u: m- i │ │ 42.Introduction to the full Bayesian approach [12 min].srt
5 s" l( ]( B | │ │ 43.The Bayesian interpretation of weight decay [11 min].mp4
6 m6 ], j( O: S │ │ 43.The Bayesian interpretation of weight decay [11 min].srt
/ R" _- X4 x, T4 Y+ U │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].mp4
0 \1 h( o# ^6 `; O1 R" J │ │ 44.MacKay's quick and dirty method of setting weight costs [4 min].srt
( l5 ?$ ^ j. i) N │ │ 45.Why it helps to combine models [13 min].mp4
. B* u7 ?5 q* g d0 i" _2 _) a# ? │ │ 45.Why it helps to combine models [13 min].srt' D: ^& p! a V- |
│ │ 46.Mixtures of Experts [13 min].mp4* ]% U; w3 Y$ ?3 ?
│ │ 46.Mixtures of Experts [13 min].srt
8 u5 R4 J* Y5 S │ │ 47.The idea of full Bayesian learning [7 min].mp4) T$ r0 g! o9 J* F: {- w/ y
│ │ 47.The idea of full Bayesian learning [7 min].srt! C5 N/ @. Y) _2 u8 S% O5 b- ]
│ │ 48.Making full Bayesian learning practical [7 min].mp4) }$ i) w$ t+ [) ~0 q. c1 V# M
│ │ 48.Making full Bayesian learning practical [7 min].srt% }! e3 O/ V1 H% y. x* {
│ │ 49.Dropout [9 min].mp4' ?* ^* \2 T" B
│ │ 49.Dropout [9 min].srt0 F. T! _8 X% \! p' T' G
│ │ 5.Three types of learning [8 min].mp46 Y! f4 w8 m; B2 i# b* H; C
│ │ 5.Three types of learning [8 min].srt. N2 a$ a0 T! L; _
│ │ 50.Hopfield Nets [13 min].mp4
$ ^$ d# `4 q+ h! b [ h │ │ 50.Hopfield Nets [13 min].srt
% S# r p) i0 _3 F e; h4 d; D │ │ 51.Dealing with spurious minima [11 min].mp47 x# r2 s* k5 `/ Z/ o3 |
│ │ 51.Dealing with spurious minima [11 min].srt
: Y. X; F# S2 l9 Z2 p0 \, g │ │ 52.Hopfield nets with hidden units [10 min].mp4- E5 ~# g9 Z1 d5 U$ E! k' l3 x
│ │ 52.Hopfield nets with hidden units [10 min].srt
2 u6 I, w9 G) C3 ?* h% } │ │ 53.Using stochastic units to improv search [11 min].mp4+ H& d6 i6 Q. ^
│ │ 53.Using stochastic units to improv search [11 min].srt* T8 |+ d& E/ `; q: \- J. ~
│ │ 54.How a Boltzmann machine models data [12 min].mp4
4 g) ~! l0 y1 ?5 r w- l │ │ 54.How a Boltzmann machine models data [12 min].srt
, M2 c, _/ M. R% m' R3 u │ │ 55.Boltzmann machine learning [12 min].mp4
- U a9 x- r* n' ^3 B │ │ 55.Boltzmann machine learning [12 min].srt4 P+ L1 ~- R3 X* O( P
│ │ 57.Restricted Boltzmann Machines [11 min].mp4- {5 g+ b9 U' l! j) U
│ │ 57.Restricted Boltzmann Machines [11 min].srt$ _5 ?9 D: l6 A/ E+ ]# c
│ │ 58.An example of RBM learning [7 mins].mp48 J9 ^$ c6 {, @8 j# ]" B
│ │ 58.An example of RBM learning [7 mins].srt7 D: o7 Y5 H: M! y$ c! ^( L
│ │ 59.RBMs for collaborative filtering [8 mins].mp4
. n+ v, @3 T+ d) L) F │ │ 59.RBMs for collaborative filtering [8 mins].srt
. h% Q' A; b* B │ │ 6.Types of neural network architectures [7 min].mp4
3 G. V& _- K. [, ` │ │ 6.Types of neural network architectures [7 min].srt! a; `) c2 J, X2 q/ I
│ │ 60.The ups and downs of back propagation [10 min].mp4
" ?9 W0 C" l+ J( x$ { │ │ 60.The ups and downs of back propagation [10 min].srt
3 L. ~: B( w. t `- |2 U# C$ N │ │ 61.Belief Nets [13 min].mp4
# B% t! ^ R* ^3 Y5 o │ │ 61.Belief Nets [13 min].srt
( Q5 z! y* S/ B. }$ \) V7 S │ │ 62.Learning sigmoid belief nets [12 min].mp4- I. e& `0 h- z9 O
│ │ 62.Learning sigmoid belief nets [12 min].srt
9 E+ @7 |9 n9 Z( ]0 [' o5 k │ │ 63.The wake-sleep algorithm [13 min].mp4& i2 X" s: O: I6 P
│ │ 63.The wake-sleep algorithm [13 min].srt
7 b* H( R7 y/ X7 m' I │ │ 64.Learning layers of features by stacking RBMs [17 min].mp4' k7 m6 X( K! _/ ^* s2 _+ U
│ │ 64.Learning layers of features by stacking RBMs [17 min].srt
7 W5 D2 H, S# P" i │ │ 65.Discriminative learning for DBNs [9 mins].mp4/ R [' W. n6 e Z/ W' ]. i9 \
│ │ 65.Discriminative learning for DBNs [9 mins].srt' e4 a9 |8 W8 L1 Y, i4 t
│ │ 66(1).What happens during discriminative fine-tuning
* {: ?2 Y. b) F' P9 K% o0 | │ │ 66.What happens during discriminative fine-tuning7 }& B( u! @' B5 o! i
│ │ 67.Modeling real-valued data with an RBM [10 mins].mp4
! A/ }& i* p' E2 C1 f5 p' u1 K │ │ 67.Modeling real-valued data with an RBM [10 mins].srt3 `4 b, b, D5 a3 Y2 n
│ │ 69.From PCA to autoencoders [5 mins].mp4
) b L+ d1 t1 }7 z) J; Y │ │ 69.From PCA to autoencoders [5 mins].srt1 g; T$ x$ M8 Y0 m' r
│ │ 70.Deep auto encoders [4 mins].mp4
$ U6 D, i4 b" j; R │ │ 70.Deep auto encoders [4 mins].srt0 e6 G* X& Y+ R
│ │ 71.Deep auto encoders for document retrieval [8 mins].mp4+ K$ S0 o9 J7 K4 W% C. ?9 K; H! U
│ │ 71.Deep auto encoders for document retrieval [8 mins].srt# J' S+ F; \* w' ~' d# K0 B# c9 {
│ │ 72.Semantic Hashing [9 mins].mp46 A- h( U# W' l
│ │ 72.Semantic Hashing [9 mins].srt0 R2 n8 u& [' C2 {: d
│ │ 73.Learning binary codes for image retrieval [9 mins].mp4. F* m- P# `, @, N
│ │ 73.Learning binary codes for image retrieval [9 mins].srt {6 K5 M# R! ]4 g, U/ H7 X
│ │ 74.Shallow autoencoders for pre-training [7 mins].mp4
. [2 Z2 p4 J6 ?0 r( S │ │ 74.Shallow autoencoders for pre-training [7 mins].srt
5 I+ e" K" d, z" O; |# T; z" T │ │ 8.A geometrical view of perceptrons [6 min].mp4, e u! f0 O1 X& x, _6 Z' b+ k
│ │ 8.A geometrical view of perceptrons [6 min].srt
0 q- b6 s1 z, e* D │ │ 9.Why the learning works [5 min].mp49 v6 r. P; n7 E7 Y! [7 u9 Y3 H6 p
│ │ 9.Why the learning works [5 min].srt
6 n# h9 u0 j, b( o3 U │ │
2 S& }- s" p/ B* h" f │ └─neuralnets-2012-001
; ?9 I0 a R: d3 Q │ ├─01_Lecture1) D2 k8 `" Y1 K# ^5 ]8 ?
│ │ 01_Why_do_we_need_machine_learning_13_min.mp4- e* ]. D! ^, C; K6 z. T ]' m
│ │ 01_Why_do_we_need_machine_learning_13_min.pdf9 n2 l/ Y5 D. g! b& V
│ │ 01_Why_do_we_need_machine_learning_13_min.pptx% \- g2 b K2 F4 F) P
│ │ 01_Why_do_we_need_machine_learning_13_min.srt
: M& p: j" N# v │ │ 01_Why_do_we_need_machine_learning_13_min.txt, S) U2 t, I9 n4 O
│ │ 02_What_are_neural_networks_8_min.mp4- G. E- E: o- C& F/ S
│ │ 02_What_are_neural_networks_8_min.srt
! n1 R( g0 l& Y% m, r │ │ 02_What_are_neural_networks_8_min.txt$ ~. B; J2 f& G2 d
│ │ 03_Some_simple_models_of_neurons_8_min.mp41 [$ r2 n" \4 C
│ │ 03_Some_simple_models_of_neurons_8_min.srt
& a- v4 k t* L! { │ │ 03_Some_simple_models_of_neurons_8_min.txt
6 d X i9 _2 S4 s i │ │ 04_A_simple_example_of_learning_6_min.mp47 G& g2 ^1 V9 V, R3 Z. i% k' G! Y3 R
│ │ 04_A_simple_example_of_learning_6_min.srt( ` \+ M) ^- S9 w$ V
│ │ 04_A_simple_example_of_learning_6_min.txt/ t. W) C9 @7 |: r
│ │ 05_Three_types_of_learning_8_min.mp4
; w% J- ?9 M% n- r+ p, L) r │ │ 05_Three_types_of_learning_8_min.srt% c. I: W7 x& n4 J! y7 v
│ │ 05_Three_types_of_learning_8_min.txt0 R2 z O; R3 e# @1 B
│ │
( @" l; ^! Y* H4 y( E q1 ], l │ ├─02_Lecture2
9 A/ V. U* o7 s- K& y4 c) |. A │ │ 03_A_geometrical_view_of_perceptrons_6_min.mp48 w* u# c C' w
│ │ 03_A_geometrical_view_of_perceptrons_6_min.srt6 e2 V3 R. L5 f, Q
│ │ 03_A_geometrical_view_of_perceptrons_6_min.txt7 ^2 J6 g9 H9 C* N
│ │ 04_Why_the_learning_works_5_min.mp4
- H( y) Q. h3 O* u. [ │ │ 04_Why_the_learning_works_5_min.srt
' q4 I4 m$ z6 T( a7 y+ G │ │ 04_Why_the_learning_works_5_min.txt- b: |+ N9 I; u; \2 G1 c! I
│ │ 05_What_perceptrons_cant_do_15_min.mp4
7 a8 A' W7 C# }& e% S/ u. j p │ │ 05_What_perceptrons_cant_do_15_min.srt5 {& Q# W! C- V# y7 B
│ │ 05_What_perceptrons_cant_do_15_min.txt
$ ~/ H) o# Y' F! e9 h* `4 S# `9 H │ │
. }5 C% S* b& p │ ├─03_Lecture3
- @& k5 z8 O" T6 l# q │ │ 04_The_backpropagation_algorithm_12_min.mp4
( g/ y! B8 J) c7 K* u │ │ 04_The_backpropagation_algorithm_12_min.pdf
R+ c- L& u. \- Y │ │ 04_The_backpropagation_algorithm_12_min.srt' \, f3 c3 k ^8 s# c: _+ P
│ │ 04_The_backpropagation_algorithm_12_min.txt O/ ~. N" @3 }8 T. s) J9 r3 g
│ │ ' s* L* y5 K1 n* E+ _
│ ├─04_Lecture4
1 j6 S" O5 K: A* G8 K# ~" Z A │ │ 01_Learning_to_predict_the_next_word_13_min.mp40 _* e, N0 s7 N- |. P
│ │ 01_Learning_to_predict_the_next_word_13_min.pdf
* P9 C7 r* W% ^0 `% F: s │ │ 01_Learning_to_predict_the_next_word_13_min.pptx
- X* L8 s$ e1 k, | │ │ 01_Learning_to_predict_the_next_word_13_min.srt4 B8 b& h# K/ u- t
│ │ 01_Learning_to_predict_the_next_word_13_min.txt
2 b5 g r5 H" g, o1 g# X5 @ │ │ 04_Neuro-probabilistic_language_models_8_min.mp4
, k3 w7 R) b6 G" A6 W; N │ │ 04_Neuro-probabilistic_language_models_8_min.pdf n, u9 C6 D5 y+ L- g( j' e# x3 F
│ │ 04_Neuro-probabilistic_language_models_8_min.srt
! b/ m5 k% x4 m8 s+ G' r( Y2 U │ │ 04_Neuro-probabilistic_language_models_8_min.txt3 z! D* p( W- A* P+ }! S1 K
│ │ , B/ _2 Z0 [6 f+ A n5 C, J2 W# t- B4 Y
│ ├─05_Lecture5
4 b. r p2 Z. Z: K$ M" J │ │ 01_Why_object_recognition_is_difficult_5_min.mp4
9 y3 \# R$ x' w │ │ 01_Why_object_recognition_is_difficult_5_min.pdf( B" B m1 G- U. g! c2 x! y# U
│ │ 01_Why_object_recognition_is_difficult_5_min.srt) Z8 k5 w, v! h9 [
│ │ 01_Why_object_recognition_is_difficult_5_min.txt2 V+ t6 V+ s) M
│ │ 02_Achieving_viewpoint_invariance_6_min.mp4/ y2 `0 j% a) A6 R6 Y8 o% x, k
│ │ 02_Achieving_viewpoint_invariance_6_min.srt
6 K. n) E( z+ k) h" [1 t! D8 x" { │ │ 02_Achieving_viewpoint_invariance_6_min.txt
& k; H6 \; \7 I+ p$ ^8 @ │ │ 8 L/ O9 Y9 L+ A0 V
│ ├─06_Lecture64 n& _- `$ x7 A5 V$ d3 o$ {
│ │ 01_Overview_of_mini-batch_gradient_descent.mp4# F' J- y8 C+ f: s* i! B
│ │ 01_Overview_of_mini-batch_gradient_descent.pdf- N! `" f6 H4 m) E$ x( ]! I
│ │ 01_Overview_of_mini-batch_gradient_descent.pptx
0 F1 R* \( Z0 y' Q' D9 S6 W2 X │ │ 01_Overview_of_mini-batch_gradient_descent.srt' f! C4 z& ^4 P# e" M5 B
│ │ 01_Overview_of_mini-batch_gradient_descent.txt
6 Z; [0 K5 ~% c: X b3 h( {- X │ │ 03_The_momentum_method.mp4
+ |/ i2 \( H( Y1 m │ │ 03_The_momentum_method.srt
" w: h, e5 r4 X+ ]# v3 r │ │ 03_The_momentum_method.txt- m2 T d# O8 O$ P/ j- E
│ │ 8 } ?1 ?) O& a! X$ g
│ ├─07_Lecture7
- Z; R" p" O5 [ │ │ 01_Modeling_sequences-_A_brief_overview.mp4
9 ^$ k3 z: q! _8 Y0 Y- g8 R, S' L/ _ │ │ 01_Modeling_sequences-_A_brief_overview.pdf
0 c) ?* b2 t# B! v" v) G │ │ 01_Modeling_sequences-_A_brief_overview.pptx
- y' A3 c& p; P& N │ │ 01_Modeling_sequences-_A_brief_overview.srt
2 d P' [& l$ \( V) E! v │ │ 01_Modeling_sequences-_A_brief_overview.txt3 v* W, H+ }6 N; ~8 V! y2 C. N! X
│ │ 02_Training_RNNs_with_back_propagation.mp4& u8 h# C( P. K' {: ^- l* f4 n
│ │ 02_Training_RNNs_with_back_propagation.srt
0 G) o4 v k5 U& p4 w/ B' t0 G8 \ │ │ 02_Training_RNNs_with_back_propagation.txt# T/ i& d$ a- A: P
│ │ 03_A_toy_example_of_training_an_RNN.mp4
: R2 s x5 m+ u Q7 d8 g# P9 @ │ │ 03_A_toy_example_of_training_an_RNN.srt
" m/ P! d6 k0 {$ K" O. R; N9 a8 H │ │ 03_A_toy_example_of_training_an_RNN.txt
; H+ i% q; x" X* ? │ │ 04_Why_it_is_difficult_to_train_an_RNN.mp4$ ], z% C d: [( u0 w4 y; Y
│ │ 04_Why_it_is_difficult_to_train_an_RNN.srt' f+ ]( O! R% ?3 o9 e3 v H- f
│ │ 04_Why_it_is_difficult_to_train_an_RNN.txt5 m& X Q* N' _5 J c' V* ?
│ │ 05_Long-term_Short-term-memory.mp4
( {- ^1 a5 E: I% _; j5 A8 u( ? │ │ 05_Long-term_Short-term-memory.pdf
3 [1 u* R* W" E │ │ 05_Long-term_Short-term-memory.srt
* V( r H3 ]/ S. z; h& F; G3 s │ │ 05_Long-term_Short-term-memory.txt
9 Y9 I! p, V @- b1 j9 ^9 C │ │
4 D7 }* R5 P6 W* i% K- s/ T' @ │ ├─08_Lecture8- ^$ d7 P! k' P
│ │ 04_Echo_State_Networks_9_min.mp4
8 o, N+ m6 n4 t6 O9 `5 w7 T$ Q5 t; g7 P │ │ 04_Echo_State_Networks_9_min.srt
5 m+ G9 W* `! G) X* Z* f+ T: z8 m9 @# m │ │ 04_Echo_State_Networks_9_min.txt
?' t) B9 L8 l) D8 I; r │ │ ) T* H8 A9 }8 S8 N0 X* i) ]" T; [
│ ├─09_Lecture95 u2 O: }' U: }+ h5 g v$ N: u
│ │ 02_Limiting_the_size_of_the_weights_6_min.mp4# U2 _& _5 J7 Q* w
│ │ 02_Limiting_the_size_of_the_weights_6_min.srt/ x6 N3 h7 e1 T/ L$ p& B9 ^
│ │ 02_Limiting_the_size_of_the_weights_6_min.txt+ w9 d# x! n& @
│ │ 03_Using_noise_as_a_regularizer_7_min.mp49 ~0 T0 a% ?. c9 V. J9 r
│ │ 03_Using_noise_as_a_regularizer_7_min.srt6 _. R7 w* E* y0 B# o8 S/ z
│ │ 03_Using_noise_as_a_regularizer_7_min.txt+ l4 }& l$ B+ X
│ │ , \# S& l6 H: H& E" w0 H6 @- H
│ ├─10_Lecture10% d% N) f% q7 v8 L
│ │ 01_Why_it_helps_to_combine_models_13_min.mp4
% b4 V7 c; n4 Z& |# m2 f( Q; Z │ │ 01_Why_it_helps_to_combine_models_13_min.pdf! y7 W0 |- a/ ?# P" c; P V
│ │ 01_Why_it_helps_to_combine_models_13_min.pptx
# [4 J f2 w" T3 q8 [+ g! F │ │ 01_Why_it_helps_to_combine_models_13_min.srt1 ]- u0 n K, |" q) B# F" b3 B
│ │ 01_Why_it_helps_to_combine_models_13_min.txt$ V& h/ B4 Z+ `$ K) e" I
│ │ 02_Mixtures_of_Experts_13_min.mp4
- q$ t$ m7 g2 m4 v │ │ 02_Mixtures_of_Experts_13_min.pdf( u& }, c8 l* M: T" P: ]& Q
│ │ 02_Mixtures_of_Experts_13_min.srt
+ t" |- r* K0 y, ~6 I7 g │ │ 02_Mixtures_of_Experts_13_min.txt
- L- E- B& r0 f; W0 G: D$ W; Q │ │ 03_The_idea_of_full_Bayesian_learning_7_min.mp4; b# i! z& P$ E/ m5 f& o. K
│ │ 03_The_idea_of_full_Bayesian_learning_7_min.srt
. e- A g8 }9 e6 n! o% w, c │ │ 03_The_idea_of_full_Bayesian_learning_7_min.txt- N# \/ t6 l" Q1 x( `6 x
│ │ 05_Dropout_9_min.mp4: ?7 s1 r# n7 C4 S Z
│ │ 05_Dropout_9_min.pdf2 Z$ z( X; F" g
│ │ 05_Dropout_9_min.srt
% g" u/ k, Q) s; q/ v X │ │ 05_Dropout_9_min.txt
% x# e4 T! u( U; C2 \ │ │
2 H$ }9 t0 \+ ]2 d# h' [; r │ ├─11_Lecture11
% w2 a$ K5 Q' {& z+ J* S │ │ 01_Hopfield_Nets_13_min.mp4! ^" a9 P g: |1 t% u1 i7 p
│ │ 01_Hopfield_Nets_13_min.pdf
! F! d9 V! ~9 b7 ~5 V │ │ 01_Hopfield_Nets_13_min.pptx+ W5 p. h" h& J. i$ a
│ │ 01_Hopfield_Nets_13_min.srt
0 ^- ?9 c7 M0 d; Y │ │ 01_Hopfield_Nets_13_min.txt
2 m2 @/ F, U. ^. |6 b9 w* Y │ │ 02_Dealing_with_spurious_minima_11_min.mp4, l; I4 [( Q* k( v, y y- i
│ │ 02_Dealing_with_spurious_minima_11_min.srt3 c. [6 O* y- ^$ s4 ?1 w; `
│ │ 02_Dealing_with_spurious_minima_11_min.txt4 b& d: u. \1 y! g) M, i
│ │ 03_Hopfield_nets_with_hidden_units_10_min.mp4; L: F' A8 j7 F
│ │ 03_Hopfield_nets_with_hidden_units_10_min.srt# _& @$ P2 w/ n6 z) L/ ]: S0 {* M
│ │ 03_Hopfield_nets_with_hidden_units_10_min.txt
4 r1 [4 G: {$ N( V9 V$ R0 W' o8 L │ │
4 `% H6 a/ O1 o; p j! n- K │ ├─12_Lecture123 a: N1 d; J" s k! z6 U
│ │ 01_Boltzmann_machine_learning_12_min.mp4
' ~! T6 p; @0 }# v4 d │ │ 01_Boltzmann_machine_learning_12_min.pdf
' |( i/ B0 S& P: B3 c W │ │ 01_Boltzmann_machine_learning_12_min.pptx! Z$ I Z, ?' Y2 x3 m' i. a; p- k
│ │ 01_Boltzmann_machine_learning_12_min.srt
) l5 n' n6 C0 m4 S' L │ │ 01_Boltzmann_machine_learning_12_min.txt
$ e/ g# y. p) P+ u) z │ │ 03_Restricted_Boltzmann_Machines_11_min.mp4
: C6 N" ~: c: {" X8 S │ │ 03_Restricted_Boltzmann_Machines_11_min.srt" M+ @! D! K9 s: a7 R4 \( F
│ │ 03_Restricted_Boltzmann_Machines_11_min.txt; U& |3 W# p$ C/ r6 x0 Q2 R2 F
│ │ 04_An_example_of_RBM_learning_7_mins.mp4- g* Z! `5 H; V' o+ R1 {) I' W
│ │ 04_An_example_of_RBM_learning_7_mins.srt2 A5 z3 e( R; `, K4 \6 D6 D
│ │ 04_An_example_of_RBM_learning_7_mins.txt
( x' V( K- P; Y │ │ 05_RBMs_for_collaborative_filtering_8_mins.mp45 X! @7 n! W3 L: D* d" Y
│ │ 05_RBMs_for_collaborative_filtering_8_mins.srt
3 Y6 G$ |: u) ` │ │ 05_RBMs_for_collaborative_filtering_8_mins.txt
9 \% ~$ ?/ _* ]* {9 ^! J │ │ ! N, S3 q; X0 N! j3 |- I6 L
│ ├─13_Lecture13" |; W6 V) L9 U; y
│ │ 02_Belief_Nets_13_min.mp4+ x4 V! o6 |+ l! o" {/ F6 ]1 ?. I
│ │ 02_Belief_Nets_13_min.srt5 {# ~! d: \5 v# @
│ │ 02_Belief_Nets_13_min.txt0 [8 E! d- S: C( K0 Y
│ │ 03_Learning_sigmoid_belief_nets_12_min.mp49 s3 V+ d, W- {: q
│ │ 03_Learning_sigmoid_belief_nets_12_min.pdf
! x* P+ H% {- { │ │ 03_Learning_sigmoid_belief_nets_12_min.srt
+ G k; W& @1 L9 W9 J+ J; G. w │ │ 03_Learning_sigmoid_belief_nets_12_min.txt4 X4 K# p$ w- c. b
│ │ 04_The_wake-sleep_algorithm_13_min.mp4
7 `, t. F8 {' w) Y │ │ 04_The_wake-sleep_algorithm_13_min.pdf0 E' y, |; ^( J- Z- G: H
│ │ 04_The_wake-sleep_algorithm_13_min.srt
: p- y! s! n8 {/ G6 Y* A. V │ │ 04_The_wake-sleep_algorithm_13_min.txt
( u3 k4 a3 `$ ^ P A2 @1 M │ │ ' m9 c5 L; w- Z9 U9 B
│ ├─14_Lecture14" n8 Q& M1 M' I6 F' a7 L; j. X) M
│ │ 02_Discriminative_learning_for_DBNs_9_mins.mp4. |) L+ U+ I5 a
│ │ 02_Discriminative_learning_for_DBNs_9_mins.srt
" N u m9 O5 T" `. r │ │ 02_Discriminative_learning_for_DBNs_9_mins.txt5 `9 M, x5 w; m7 v) V' u
│ │ ! f! r% W8 A1 E% N
│ ├─15_Lecture156 O0 J: _$ C! N- h" x0 ^
│ │ 01_From_PCA_to_autoencoders_5_mins.mp4
3 A+ g8 j2 N, L6 z7 @& c8 l) y( O │ │ 01_From_PCA_to_autoencoders_5_mins.pdf3 E2 i& |: M" e
│ │ 01_From_PCA_to_autoencoders_5_mins.pptx/ Z# O: z* `7 s' D' X
│ │ 01_From_PCA_to_autoencoders_5_mins.srt
0 p, [( N+ S% \4 w9 X# x │ │ 01_From_PCA_to_autoencoders_5_mins.txt+ E' X. A& s/ r6 n ^
│ │ 02_Deep_auto_encoders_4_mins.mp4
( s4 Z: K5 g5 g6 W4 M" {5 B7 @ │ │ 02_Deep_auto_encoders_4_mins.srt( ^8 c: r X$ z
│ │ 02_Deep_auto_encoders_4_mins.txt
1 D. o" y4 B8 `" O8 G- C. r, `* A │ │ 04_Semantic_Hashing_9_mins.mp4
8 p- Y7 ]9 K( m, f1 D9 X │ │ 04_Semantic_Hashing_9_mins.pdf
" f3 q* `, ^' P5 y) C │ │ 04_Semantic_Hashing_9_mins.srt) l- x' Z* d1 M0 o( b3 I8 S
│ │ 04_Semantic_Hashing_9_mins.txt: v9 O' e1 S8 O) s* c4 Z5 @
│ │ & z% ]# h+ @; v
│ └─16_Lecture16: D2 o8 h+ k3 j8 F
│ 04_OPTIONAL-_The_fog_of_progress_3_min.mp4
$ K' z& L: u( I$ K( [ E │ 04_OPTIONAL-_The_fog_of_progress_3_min.pdf
5 u# \$ \7 \' q6 w( H │ 04_OPTIONAL-_The_fog_of_progress_3_min.pptx' K+ f! @( V! ?5 E
│ 04_OPTIONAL-_The_fog_of_progress_3_min.srt6 Z8 j+ ?2 u* z9 d. h; b
│ 04_OPTIONAL-_The_fog_of_progress_3_min.txt8 g: s! R; O1 Q! _
│ 9 w) u7 }3 U6 W: }
├─006_Probabilistic Graphical Models4 \1 C% C% o1 c5 q
│ └─006_Probabilistic Graphical Models& {' x2 L: I3 k
│ ├─pgm-003# l w, d4 d, ^" [! F
│ │ ├─01_Introduction_and_Overview% a" o y6 ?; x" b7 x
│ │ │ 01_Welcome.mp4
$ `. c. b {' F" T0 _% E │ │ │ 01_Welcome.srt
/ e) O4 w; O: O5 X2 a+ s; ? │ │ │ 01_Welcome.txt2 X2 Y# T: V# [( C+ j9 I7 W
│ │ │ 02_Overview_and_Motivation.mp4
2 }/ i( x% v* ~, w7 o* D% \ │ │ │ 02_Overview_and_Motivation.srt& G7 s0 g1 |0 g$ R4 y, F1 S
│ │ │ 02_Overview_and_Motivation.txt4 c8 l/ B0 z9 y/ M" K) l
│ │ │ 03_Distributions.mp4! O3 I3 q% ^. S: P9 |! M8 R! d
│ │ │ 03_Distributions.srt. b' G1 S3 ^4 i; G6 z* m. J
│ │ │ 03_Distributions.txt- n' _) `. Z% M4 n' l5 C
│ │ │ 04_Factors.mp4
# }# O; @: r2 Z* g% O0 z9 I │ │ │ 04_Factors.srt
2 r S' O6 N. G │ │ │ 04_Factors.txt
- o1 H" K5 Y% Q: R3 k │ │ │
' C1 t0 s+ @9 c4 D' p& f │ │ ├─02_Bayesian_Network_Fundamentals+ V: i f4 g6 s0 b2 Q9 f
│ │ │ 01_Semantics_amp_Factorization.mp44 k3 c; s& E+ b! Y9 r8 m- x2 `, \
│ │ │ 01_Semantics_amp_Factorization.srt+ C4 i7 g6 q( c+ @2 Y
│ │ │ 01_Semantics_amp_Factorization.txt
6 B: I* |. Y0 `/ P% D │ │ │ 02_Reasoning_Patterns.mp4
8 Y! |2 [7 J/ v& f( f" D5 ~ │ │ │ 02_Reasoning_Patterns.srt R b$ _1 B* w9 j/ j" c
│ │ │ 02_Reasoning_Patterns.txt
1 U( k) S5 g1 y! ? │ │ │ 03_Flow_of_Probabilistic_Influence.mp4
' c9 P1 }/ q/ m. A │ │ │ 03_Flow_of_Probabilistic_Influence.srt% a- \" E# G' H5 `) I5 C% ]
│ │ │ 03_Flow_of_Probabilistic_Influence.txt
6 N8 D3 n* w7 ]! z' ? │ │ │ 04_Conditional_Independence.mp4; a) m5 [" p! o8 v. M
│ │ │ 04_Conditional_Independence.srt; X3 }/ g; O2 ]' ]. Z
│ │ │ 04_Conditional_Independence.txt W" ^. G, P' w8 F+ B8 m' g1 F
│ │ │ 05_Independencies_in_Bayesian_Networks.mp4# g/ U: { o) [) `, i
│ │ │ 05_Independencies_in_Bayesian_Networks.srt J+ t# U" ~( I# ~3 @
│ │ │ 05_Independencies_in_Bayesian_Networks.txt
" t2 B- n( H+ r# c& H' E# E │ │ │ 06_Naive_Bayes.mp4
- r2 @2 k# j, P v( a+ q │ │ │ 06_Naive_Bayes.srt( }% ~1 h" G( a* c8 g
│ │ │ 06_Naive_Bayes.txt5 g) T" k# d3 H8 `' f" {
│ │ │ 07_Application_-_Medical_Diagnosis.mp4
\+ T# N0 S5 d H1 ` │ │ │ 07_Application_-_Medical_Diagnosis.srt
7 \& X Z9 u: y8 f* ?: p8 \ │ │ │ 07_Application_-_Medical_Diagnosis.txt
w, n" r- P! w' N │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.mp4
7 C X5 O8 d# u$ @' i) b- u' ]0 B │ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.srt. w' V0 `& [" W! P. }, |6 M' F% ~
│ │ │ 08_Knowledge_Engineering_Example_-_SAMIAM.txt2 J! P. a: b# ^
│ │ │
- c% c3 M5 c4 f+ B* L3 J │ │ ├─03_Template_Models. G& S; S5 g8 G" s% z) ^* A
│ │ │ 01_Overview_of_Template_Models.mp4
% s& l o' Y2 ] │ │ │ 01_Overview_of_Template_Models.srt
9 B1 |$ q3 ~3 Q │ │ │ 01_Overview_of_Template_Models.txt
8 i( I) `) Z# \ │ │ │ 02_Temporal_Models_-_DBNs.mp4
/ m8 e1 @# q- k' {/ j │ │ │ 02_Temporal_Models_-_DBNs.srt
) c1 b9 F! }+ s9 @& g │ │ │ 02_Temporal_Models_-_DBNs.txt
. \% i, K0 i% j8 Y; z$ q │ │ │ 03_Temporal_Models_-_HMMs.mp49 Y7 x$ A+ B$ P/ G; d
│ │ │ 03_Temporal_Models_-_HMMs.srt
5 E" u B! F0 @2 H) r; o │ │ │ 03_Temporal_Models_-_HMMs.txt
- |; Q7 |. {, Q' o, K" G │ │ │ 04_Plate_Models.mp4
; R; Z" ^) m7 G7 I- I# z# O: q │ │ │ 04_Plate_Models.srt
$ L! q3 V! I( f/ r7 J1 Q │ │ │ 04_Plate_Models.txt
; R& s w6 M6 _& R │ │ │ * W7 Q" F+ W0 Q, g2 P5 H
│ │ ├─04_ML-class_Octave_Tutorial
& L/ `" {1 R) D' s+ W9 }+ h& t │ │ │ 01_Basic_Operations.mp4
! M1 f! P1 c% l8 ] │ │ │ 01_Basic_Operations.srt
# Y1 ?4 R# w% y$ o5 B0 P. [- j │ │ │ 01_Basic_Operations.txt1 |; ?; Z' o- W
│ │ │ 02_Moving_Data_Around.mp4/ t1 _7 s; ?9 }1 ~0 O
│ │ │ 02_Moving_Data_Around.srt* ~4 D- O7 O( \1 F4 s+ |
│ │ │ 02_Moving_Data_Around.txt0 D% t) c' v; e8 S
│ │ │ 03_Computing_On_Data.mp45 J. t8 N) l7 z& \( x
│ │ │ 03_Computing_On_Data.srt- D- I" a" q" c0 z7 h& J
│ │ │ 03_Computing_On_Data.txt
* T& W% H& X, ^8 y' B │ │ │ 04_Plotting_Data.mp4
K4 U4 |) s1 S. [/ i, P │ │ │ 04_Plotting_Data.srt! y" A: m' n% ]- p
│ │ │ 04_Plotting_Data.txt
6 A, k. m x. F W% E │ │ │ 05_Control_Statements-_for_while_if_statements.mp4
2 |6 a! P0 P8 p# F. F, o7 _! D; f │ │ │ 05_Control_Statements-_for_while_if_statements.srt
2 Z' @5 Z9 [) }$ i8 k% Y4 ^ │ │ │ 05_Control_Statements-_for_while_if_statements.txt
$ Q' S+ g2 h2 g, G$ L │ │ │ 06_Vectorization.mp4
3 k# ~. f6 e7 l: D8 O7 u0 z │ │ │ 06_Vectorization.srt0 E: s+ p3 x, h# t4 m$ `# I
│ │ │ 06_Vectorization.txt
# I( ?# ^( g9 p │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.mp4
$ G) g X5 G. l3 U# h4 e$ t. J' }( } │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.srt
% w/ U8 M( X% R; y5 O9 } │ │ │ 07_Working_on_and_Submitting_Programming_Exercises.txt6 n8 t; e/ S: B$ q! S& Y( ?$ c$ e, u
│ │ │ 6 i: P% w8 D0 P+ d' s
│ │ ├─05_Structured_CPDs8 H& J% H/ ` k( M; L6 s& d2 f
│ │ │ 01_Overview-_Structured_CPDs.mp46 Q$ N; L/ W3 Q5 B, @
│ │ │ 01_Overview-_Structured_CPDs.srt) d8 R; y1 }/ W2 t6 b; |7 j& D
│ │ │ 01_Overview-_Structured_CPDs.txt" O2 D7 @3 P! _/ J/ F w( u
│ │ │ 02_Tree-Structured_CPDs.mp4
5 ~. |' U" B/ _7 w X │ │ │ 02_Tree-Structured_CPDs.srt
/ U4 T" ?& @* D. @; Q │ │ │ 02_Tree-Structured_CPDs.txt
' ?, q$ m+ J( g R0 P7 Y │ │ │ 03_Independence_of_Causal_Influence.mp43 x6 s8 `( }: ~1 u% r
│ │ │ 03_Independence_of_Causal_Influence.srt- D+ n$ x9 \. E( ? g' z5 U% r
│ │ │ 03_Independence_of_Causal_Influence.txt
- d: g# N" p0 A │ │ │ 04_Continuous_Variables.mp4( N2 B Z7 q$ A6 T- c% C
│ │ │ 04_Continuous_Variables.srt
' d! @/ s; x2 y. ? │ │ │ 04_Continuous_Variables.txt4 K) G% Q" K5 A
│ │ │ : S% x* D, o) _; b7 ~) A
│ │ ├─06_Markov_Network_Fundamentals- v M: P' O) ]
│ │ │ 01_Pairwise_Markov_Networks.mp4# K j( [+ S8 X/ R* A \) R( E
│ │ │ 01_Pairwise_Markov_Networks.srt
9 |. u" r+ X' K │ │ │ 01_Pairwise_Markov_Networks.txt% L+ }& r' I7 \5 k
│ │ │ 02_General_Gibbs_Distribution.mp4& P* H- ]- M& d' a/ ~) c% r4 T
│ │ │ 02_General_Gibbs_Distribution.srt
$ ]* C, F/ j7 ]' w; u; F │ │ │ 02_General_Gibbs_Distribution.txt7 ?/ d* _# W2 ^( v) U' K
│ │ │ 03_Conditional_Random_Fields.mp4, Z4 z' Y1 L( g0 p+ h6 ~
│ │ │ 03_Conditional_Random_Fields.srt
$ t9 |( |& A' c0 `% ] │ │ │ 03_Conditional_Random_Fields.txt
5 q% ^0 w1 Q4 ^; {. e- W │ │ │ 04_Independencies_in_Markov_Networks.mp4% b6 J9 `% M* F# E3 n# }
│ │ │ 04_Independencies_in_Markov_Networks.srt
) B( z T5 f$ H0 ~! z$ @ │ │ │ 04_Independencies_in_Markov_Networks.txt
j) E' D3 ]* ` │ │ │ 05_I-maps_and_perfect_maps.mp4 G7 I9 [' K( }2 t
│ │ │ 05_I-maps_and_perfect_maps.srt
; a+ n0 Z; L0 o9 ` │ │ │ 05_I-maps_and_perfect_maps.txt
% j2 t8 Z% U% N1 F+ t │ │ │ 06_Log-Linear_Models.mp4
8 u, q# F1 S p" D6 ~. w │ │ │ 06_Log-Linear_Models.srt
5 R: G3 Z: K3 ^; V2 I, N: C │ │ │ 06_Log-Linear_Models.txt$ t( L: o% k" n! u4 W$ e- T+ y
│ │ │ 07_Shared_Features_in_Log-Linear_Models.mp4 @) n* q. J- c
│ │ │ 07_Shared_Features_in_Log-Linear_Models.srt/ E6 q; O3 M5 R c0 q9 Q
│ │ │ 07_Shared_Features_in_Log-Linear_Models.txt
. m- n t* ~' Y, }: T) k │ │ │ ) P! J4 d5 O9 C7 s
│ │ ├─07_Representation_Wrapup-_Knowledge_Engineering$ Y0 r" ?, V: T+ u
│ │ │ 01_Knowledge_Engineering.mp4
2 m/ b d" Z0 d* J$ T │ │ │ 01_Knowledge_Engineering.srt" O- K) y" v0 X8 y/ w# ]: J
│ │ │ 01_Knowledge_Engineering.txt
U1 r# e; q g6 \( G% v. ~ │ │ │
. T; Q; o% t3 H/ [; M5 \. M$ k S │ │ ├─08_Inference-_Variable_Elimination! m; e! P* `, Z$ t" M0 g3 a4 V5 [* M9 |
│ │ │ 01_Overview-_Conditional_Probability_Queries.mp4
0 g" X6 \; H# _# \/ X: [: V │ │ │ 01_Overview-_Conditional_Probability_Queries.srt
6 G. j: ~' @' h │ │ │ 01_Overview-_Conditional_Probability_Queries.txt
* m* r; n. `9 Z! ] │ │ │ 02_Overview-_MAP_Inference.mp42 C4 [/ A6 e. h& F. G1 c4 U
│ │ │ 02_Overview-_MAP_Inference.srt& l* y: U/ J. r' c2 q6 D8 R# \
│ │ │ 02_Overview-_MAP_Inference.txt4 P7 W9 ]6 U0 X3 H! @5 Y5 t9 z _# l
│ │ │ 03_Variable_Elimination_Algorithm.mp4
) ]+ M0 \; }* _. t" a │ │ │ 03_Variable_Elimination_Algorithm.srt, O$ d/ D" Z" z: e) g$ ]8 o0 S+ f! ?1 o
│ │ │ 03_Variable_Elimination_Algorithm.txt# l/ A$ E! y2 V- [
│ │ │ 04_Complexity_of_Variable_Elimination.mp4
3 K6 d; _$ {" R% z% y, T │ │ │ 04_Complexity_of_Variable_Elimination.srt0 W& v2 p' n+ x6 y- R$ _8 [6 C, u
│ │ │ 04_Complexity_of_Variable_Elimination.txt
, e9 B6 L2 K7 S( R │ │ │ 06_Finding_Elimination_Orderings.mp4
) n8 `5 R$ O) M; Q+ h1 d, o/ Q │ │ │ 06_Finding_Elimination_Orderings.srt6 c3 ?+ i+ n/ u7 |2 X
│ │ │ 06_Finding_Elimination_Orderings.txt
: a- h/ C2 m$ D5 {# a │ │ │
! s3 M+ p3 b9 z* g+ Q. w │ │ ├─09_Inference-_Belief_Propagation_Part_16 y+ z" G- L7 @: Z
│ │ │ 01_Belief_Propagation.mp4
$ P9 ?1 s) I& {! n │ │ │ 01_Belief_Propagation.srt6 k3 v, O% h$ w7 X0 j9 w' Y
│ │ │ 01_Belief_Propagation.txt
6 o$ ^# w# J3 |# B │ │ │ 02_Properties_of_Cluster_Graphs.mp4" y0 f$ c' Q) `1 [* F# g$ Y
│ │ │ 02_Properties_of_Cluster_Graphs.srt" J9 t6 a' z: E$ Z, m0 O
│ │ │ 02_Properties_of_Cluster_Graphs.txt( ^4 I+ u% i! U2 J+ m9 F4 Y$ w
│ │ │ 3 B9 L$ Q" Q E# \0 `4 C
│ │ ├─10_Inference-_Belief_Propagation_Part_2, }; l7 s+ q5 n2 U" I$ n
│ │ │ 01_Properties_of_Belief_Propagation.mp4. g( S/ d" R- G Y, r4 @
│ │ │ 01_Properties_of_Belief_Propagation.srt* \3 a4 Y% ~: m: F. }
│ │ │ 01_Properties_of_Belief_Propagation.txt' A4 B \7 m7 W+ w( U4 I
│ │ │ 02_Clique_Tree_Algorithm_-_Correctness.mp4
3 V; I- F2 v* w& y) ` │ │ │ 02_Clique_Tree_Algorithm_-_Correctness.srt% |# P; A% k7 Q, e0 q) K# n
│ │ │ 02_Clique_Tree_Algorithm_-_Correctness.txt# H# b; `( e- V2 u* S w( e
│ │ │ 03_Clique_Tree_Algorithm_-_Computation.mp4
9 [# C+ o6 a. z/ i) g; ~ │ │ │ 03_Clique_Tree_Algorithm_-_Computation.srt. w0 z4 {) D# z) H: z
│ │ │ 03_Clique_Tree_Algorithm_-_Computation.txt
0 n8 \9 f9 `7 F │ │ │ 04_Clique_Trees_and_Independence.mp4
/ |- B8 `+ e( G: z0 r1 s% i: I+ f; C9 X │ │ │ 04_Clique_Trees_and_Independence.srt0 |" l9 q! u: K& h5 h5 i
│ │ │ 04_Clique_Trees_and_Independence.txt% G" V- H/ t& R4 ]
│ │ │ 05_Clique_Trees_and_VE.mp4
- h/ K9 e+ t& ~" l- k9 L2 p │ │ │ 05_Clique_Trees_and_VE.srt# j5 u! K' Q7 L
│ │ │ 05_Clique_Trees_and_VE.txt
) b; `2 K ^( _( `( z( Q │ │ │ 06_BP_In_Practice.mp4
0 P- ~" A, G) N │ │ │ 06_BP_In_Practice.srt: q2 F ]; @+ Q; v9 n3 [+ |2 g6 d
│ │ │ 06_BP_In_Practice.txt) O3 S1 Z: U! Y4 G/ y4 n
│ │ │ 07_Loopy_BP_and_Message_Decoding.mp4
# R, K: Q5 j. M; v, B0 y, j( R │ │ │ 07_Loopy_BP_and_Message_Decoding.srt+ P H" s4 W- s* H4 P2 L: Z6 X
│ │ │ 07_Loopy_BP_and_Message_Decoding.txt
u2 z) A/ R# { │ │ │ ( d! V& i. X1 Z B( m1 x' H
│ │ ├─11_Inference-_MAP_Estimation_Part_1% F$ Z. D2 Q) A/ ]" A
│ │ │ 01_Max_Sum_Message_Passing.mp4
& X. u" W5 H0 t5 @0 } │ │ │ 01_Max_Sum_Message_Passing.srt
: ?2 C9 k+ f9 K7 S0 } g+ j K │ │ │ 01_Max_Sum_Message_Passing.txt; P$ t0 d3 r, {1 h. ^$ ~: M" |
│ │ │ 02_Finding_a_MAP_Assignment.mp4
7 O5 N, R" x8 \0 ^8 L' I" ]" b │ │ │ 02_Finding_a_MAP_Assignment.srt$ D: {/ d# \% O, \" i
│ │ │ 02_Finding_a_MAP_Assignment.txt
/ p' x7 o0 v) _- e( V/ z8 L │ │ │ 2 x8 z8 I7 Q+ ~! Q
│ │ ├─12_Inference-_MAP_Estimation_Part_2
* ?6 k+ A9 q# b! l. L& }+ O │ │ │ 01_Tractable_MAP_Problems.mp4
- C; f. o% w3 j% |6 @( x p │ │ │ 01_Tractable_MAP_Problems.srt
1 n4 k8 k4 d6 {* p1 k+ t │ │ │ 01_Tractable_MAP_Problems.txt8 ^' D3 Q) S2 U. X
│ │ │ 02_Dual_Decomposition_-_Intuition.mp4. b v( V4 ^, H Q, A
│ │ │ 02_Dual_Decomposition_-_Intuition.srt3 N. k/ d( Y8 s9 [. |, C7 O' m
│ │ │ 02_Dual_Decomposition_-_Intuition.txt
; x9 i3 N( e) g4 S9 Q │ │ │ 03_Dual_Decomposition_-_Algorithm.mp4" R3 ?) W- T! r
│ │ │ 03_Dual_Decomposition_-_Algorithm.srt2 s. n2 ^! V. w6 Q- y- X; G
│ │ │ 03_Dual_Decomposition_-_Algorithm.txt3 _2 c$ y2 k2 a
│ │ │
, ~0 ^' F `& b │ │ ├─13_Inference-_Sampling_Methods
( @. k2 }3 a4 M' O6 N' f │ │ │ 01_Simple_Sampling.mp4
+ u8 H! l! H0 B5 y7 i) a2 _% } │ │ │ 01_Simple_Sampling.srt
- |' C4 N9 f. Z% X u3 C: W) I: ~ │ │ │ 01_Simple_Sampling.txt
* ^& Y6 ^' @5 b% U5 t1 z │ │ │ 02_Markov_Chain_Monte_Carlo.mp4
7 k3 x7 v5 C4 b% M0 `, c5 t │ │ │ 02_Markov_Chain_Monte_Carlo.srt
, C- u5 d a2 C0 v │ │ │ 02_Markov_Chain_Monte_Carlo.txt% i9 F' H" }+ y- s$ w* W7 z
│ │ │ 03_Using_a_Markov_Chain.mp4
+ e: E9 a1 \' X/ v │ │ │ 03_Using_a_Markov_Chain.srt
- m7 B0 Q8 N* x u* L/ b( G │ │ │ 03_Using_a_Markov_Chain.txt, ?" i& s' l5 K/ u) K, l( n9 n
│ │ │ 04_Gibbs_Sampling.mp4: d; t" M/ I9 }0 j7 w% l
│ │ │ 04_Gibbs_Sampling.srt
- ? s1 Q& g8 v* z │ │ │ 04_Gibbs_Sampling.txt
5 n2 d& @5 r" S1 I2 ] │ │ │ 05_Metropolis_Hastings_Algorithm.mp43 F& f/ L: m! c# b
│ │ │ 05_Metropolis_Hastings_Algorithm.srt6 R! ~) }8 ]! o2 _+ ^" e$ ^8 @+ |
│ │ │ 05_Metropolis_Hastings_Algorithm.txt6 @) f0 [# Q V5 A7 o1 @
│ │ │ : F2 D. m2 I0 w% {+ I3 ?
│ │ ├─14_Inference-_Temporal_Models_and_Wrap-up/ D/ o2 f6 Z- V8 X/ s# Z
│ │ │ 01_Inference_in_Temporal_Models.mp4
' Z8 Y0 x2 ?/ {- u. B* D8 x │ │ │ 01_Inference_in_Temporal_Models.srt
8 ^, {! M* I2 R5 n" Y p │ │ │ 01_Inference_in_Temporal_Models.txt
! |. u) v4 j- n/ Q! `& w: q │ │ │ 02_Inference-_Summary.mp4
/ y& P* p2 i3 j! B │ │ │ 02_Inference-_Summary.srt3 H/ i- h" h* e! J4 M7 G% A
│ │ │ 02_Inference-_Summary.txt4 T1 A" E$ E* f. Y# L: U8 ]
│ │ │
" _/ N @1 Q# m4 S │ │ ├─15_Decision_Theory, i$ P) `8 W& f. T, C" ~& H
│ │ │ 01_Maximum_Expected_Utility.mp4
Q3 ?9 H/ |$ @6 |' X │ │ │ 01_Maximum_Expected_Utility.srt/ \3 W3 U5 s0 e3 M
│ │ │ 01_Maximum_Expected_Utility.txt# u) `# k6 h+ x. D; ]+ F2 a
│ │ │ 02_Utility_Functions.mp4
5 D! E0 Z8 w% q' o$ r+ H# k │ │ │ 02_Utility_Functions.srt; n( r: x, d) R
│ │ │ 02_Utility_Functions.txt
Y" @; I6 [+ N) J! T& Q │ │ │ 03_Value_of_Perfect_Information.mp4
$ x1 B( l2 C4 z% B │ │ │ 03_Value_of_Perfect_Information.srt1 f* @3 I2 T6 F6 G- a3 d; v
│ │ │ 03_Value_of_Perfect_Information.txt: H9 @ T( i9 e- s/ v3 N% E
│ │ │ 2 u" K. T& y* N3 h" U
│ │ ├─16_ML-class_Revision
3 }9 ^$ A, L$ N* M% o% c5 g' | │ │ │ 01_Regularization-_The_Problem_of_Overfitting.mp4
A; m$ C6 N Q& e9 r9 j │ │ │ 01_Regularization-_The_Problem_of_Overfitting.srt, Z# ]; u/ w* H
│ │ │ 01_Regularization-_The_Problem_of_Overfitting.txt
2 K6 }( F4 P Z% D0 O# w2 e │ │ │ 02_Regularization-_Cost_Function.mp4$ Q3 Z! Z+ s+ X
│ │ │ 02_Regularization-_Cost_Function.srt# g$ L5 y/ S: ?6 `) Z
│ │ │ 02_Regularization-_Cost_Function.txt
8 C% u# P7 V# [ │ │ │ 03_Evaluating_a_Hypothesis.mp4
2 _5 i/ a- _( `" i7 j, v │ │ │ 03_Evaluating_a_Hypothesis.srt/ c2 H1 @+ x# g4 A# r
│ │ │ 03_Evaluating_a_Hypothesis.txt
/ ~5 a. {1 k7 @8 R- o% w$ o │ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.mp4; m. g, s8 D6 ^' v) S
│ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.srt$ S* b+ u b1 x" h; Q! Q) q
│ │ │ 04_Model_Selection_and_Train_Validation_Test_Sets.txt3 b% G( M+ u1 H- ~
│ │ │ 05_Diagnosing_Bias_vs_Variance.mp45 ?3 ?( A- A6 {- u; X# G
│ │ │ 05_Diagnosing_Bias_vs_Variance.srt
; h: Q/ D! y) Q0 u │ │ │ 05_Diagnosing_Bias_vs_Variance.txt0 E# C' k `8 t5 \4 T" u# K
│ │ │ 06_Regularization_and_Bias_Variance.mp4
c3 X4 i- m" g8 t1 h; m! ~ D │ │ │ 06_Regularization_and_Bias_Variance.srt
& I6 M7 k) ?' d% u │ │ │ 06_Regularization_and_Bias_Variance.txt4 [0 F2 E0 @( b( S( Q1 Q7 s
│ │ │
$ {7 [0 \+ }8 @; _1 \ e c! M │ │ ├─17_Learning-_Overview/ Q2 y/ X* E' v [
│ │ │ 01_Learning-_Overview.mp4
; A; |; ]1 N/ W │ │ │ 01_Learning-_Overview.srt
, F" g: H) |) r1 A5 G │ │ │ 01_Learning-_Overview.txt
! |* B$ u/ b' t9 H, D. c& t │ │ │ $ i+ [2 \0 ~8 V/ \6 \/ @: u
│ │ ├─18_Learning-_Parameter_Estimation_in_BNs+ h& H6 z5 ~5 j) R4 c
│ │ │ 01_Maximum_Likelihood_Estimation.mp45 ~6 }$ C$ }7 Y0 y
│ │ │ 01_Maximum_Likelihood_Estimation.srt
( k- Y- C/ B# E( x& A- ? │ │ │ 01_Maximum_Likelihood_Estimation.txt3 {) u) i. c: D J" ]' T4 j" u
│ │ │ 03_Bayesian_Estimation.mp4* l4 n1 m' {3 q: s5 u8 t2 ^
│ │ │ 03_Bayesian_Estimation.srt
1 d9 `& g" F' Q │ │ │ 03_Bayesian_Estimation.txt
8 [9 J3 e; C6 U9 V' n2 W7 {/ U │ │ │ 04_Bayesian_Prediction.mp4
r& h; t1 @2 w; }! ]4 k │ │ │ 04_Bayesian_Prediction.srt
; j I1 u; c, M8 l8 N# W8 { │ │ │ 04_Bayesian_Prediction.txt
% Z' m& y+ {- q; \ │ │ │
: a# e" m5 l9 i* B" P │ │ ├─19_Learning-_Parameter_Estimation_in_MNs5 L# H4 Z4 D1 v/ r
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.mp49 Q: X, Z0 \1 p( A
│ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.srt
2 F0 G7 O/ [$ f( K │ │ │ 03_MAP_Estimation_for_MRFs_and_CRFs.txt
6 B$ W/ E8 f$ c( \! T │ │ │ & p5 m; ~6 R0 S) k* m9 L9 g* W
│ │ ├─20_Structure_Learning
2 C' g" F( K8 i% L │ │ │ 01_Structure_Learning_Overview.mp4
: k X8 v7 ?! L8 L9 e; V6 V │ │ │ 01_Structure_Learning_Overview.srt& ^+ v( s' p0 D% b# r! ~
│ │ │ 01_Structure_Learning_Overview.txt, ]" G- m( p8 L- v' H5 T/ |9 K
│ │ │ 02_Likelihood_Scores.mp47 t: N G* q9 |$ }8 R* ~5 i
│ │ │ 02_Likelihood_Scores.srt4 z7 w* ~% _5 U
│ │ │ 02_Likelihood_Scores.txt) N }% M* k9 v* M5 Z4 e% U4 A
│ │ │ 03_BIC_and_Asymptotic_Consistency.mp4
" W* [% P! @/ E2 Z w) l2 { │ │ │ 03_BIC_and_Asymptotic_Consistency.srt
9 {$ `' c% ?+ |) ~4 G3 ]3 ^) m │ │ │ 03_BIC_and_Asymptotic_Consistency.txt! Y+ \3 k F; h; L6 m- x$ h
│ │ │ 04_Bayesian_Scores.mp4/ f0 s$ T$ S( l! t
│ │ │ 04_Bayesian_Scores.srt/ L$ B+ K0 V% ]# @$ \5 m
│ │ │ 04_Bayesian_Scores.txt
, X# K, _, x, ^3 O │ │ │ 05_Learning_Tree_Structured_Networks.mp48 j/ |' h+ Z& G
│ │ │ 05_Learning_Tree_Structured_Networks.srt
. ?) s" C) L8 B- `& g5 u │ │ │ 05_Learning_Tree_Structured_Networks.txt
1 M1 V0 E0 q$ z# `5 S' v │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.mp4
1 F% M7 f: b& a8 _. s r0 S9 J& }# X │ │ │ 06_Learning_General_Graphs-_Heuristic_Search.srt/ ?2 W: C3 S, G+ v) `- V" ?- S% U: Y
│ │ │ 06_Learning_General_Graphs-_Heuristic_Search.txt7 J$ t& v: K0 \6 D+ c* C( t
│ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.mp4% K7 J0 w }* `$ h$ Z
│ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.srt
, u1 y- o: t9 S4 j( Y │ │ │ 07_Learning_General_Graphs-_Search_and_Decomposability.txt
8 U6 c4 b! d" Y! G6 Q │ │ │
. d$ s+ E# g$ u7 E │ │ ├─21_Learning_With_Incomplete_Data4 L# N! u: W- Z- Q2 u4 C9 E
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.mp4/ [) J9 p4 t0 o C* g
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.srt+ _5 C3 z1 {7 a( W
│ │ │ 01_Learning_With_Incomplete_Data_-_Overview.txt% W' p* |$ e# T0 D5 Q* J9 ^
│ │ │ 02_Expectation_Maximization_-_Intro.mp4
5 o4 Z" v7 B4 C5 o9 w. q │ │ │ 02_Expectation_Maximization_-_Intro.srt
+ a- G$ p& K; [5 ] │ │ │ 02_Expectation_Maximization_-_Intro.txt9 x; @( C3 l# F/ f0 h0 j8 p( e: o
│ │ │ 03_Analysis_of_EM_Algorithm.mp4/ h k( | J9 A u$ x; P1 l4 h
│ │ │ 03_Analysis_of_EM_Algorithm.srt
~& t0 N8 g7 z7 t1 C; y │ │ │ 03_Analysis_of_EM_Algorithm.txt
- d' X+ Z) S" {5 O$ n │ │ │ 04_EM_in_Practice.mp4
+ t8 [* r- w4 } │ │ │ 04_EM_in_Practice.srt
% o$ F* A; y- L7 I- n+ E5 b │ │ │ 04_EM_in_Practice.txt
; v9 c Z' A; \# L. j │ │ │ 05_Latent_Variables.mp4( L3 }5 l$ ]/ n
│ │ │ 05_Latent_Variables.srt
+ f% H* J; D6 r# J1 R; N8 _ │ │ │ 05_Latent_Variables.txt: T) ]$ V+ q$ K) L% t$ T
│ │ │
5 [0 X% q* X3 y. j0 E │ │ ├─22_Learning-_Wrapup( t9 G6 |3 x' G, m) k9 v; I, j" t
│ │ │ 01_Summary-_Learning.mp4+ ~0 `5 F# \( A( z' t) J
│ │ │ 01_Summary-_Learning.srt; a0 C6 U% T% h c |4 `, o( H
│ │ │ 01_Summary-_Learning.txt9 i7 `( V! @8 Q- ]% t7 `8 J3 t$ z" ~
│ │ │
. U0 U5 \- Y9 r0 G6 y4 j │ │ └─23_Summary
" [- k x0 d6 p6 \ │ │ 01_Class_Summary.mp46 J2 U" } Z5 s6 J Z
│ │ 01_Class_Summary.srt# y2 E. Y( }% M$ \
│ │ 01_Class_Summary.txt, D! I0 J6 I! A1 `0 _* a
│ │
( y7 n( O& T8 D+ l │ └─Probabilistic Graphical Models - Stanford$ S' u( M6 n( w8 O/ B. L
│ 1 - 1 - Welcome! (05-35).mp4
/ ?, {" s7 S% {8 _( ^ │ 1 - 1 - Welcome! (05-35).srt) o) S1 f3 Y( U2 S9 ^3 d
│ 1 - 2 - Overview and Motivation (19-17).mp4
) _* J8 H8 k, M& k' I+ l8 p │ 1 - 2 - Overview and Motivation (19-17).srt+ O& t" c& a8 _8 l9 v
│ 1 - 3 - Distributions (04-56).mp44 n5 _( @2 F/ j
│ 1 - 3 - Distributions (04-56).srt& N8 v$ l0 \& M. Y7 _
│ 1 - 4 - Factors (06-40).mp4% f% u! O5 J" U+ l+ K( L
│ 1 - 4 - Factors (06-40).srt
3 _, ~: W0 G$ J0 i! y" ]( } │ 10 - 4 - Clique Trees and Independence (15-21).mp4( p5 Y# } R" S. ^$ r( ?( p$ l
│ 10 - 4 - Clique Trees and Independence (15-21).srt
$ ?& N9 x) A. R- y0 t │ 10 - 5 - Clique Trees and VE (16-17).mp4
9 l* M9 V6 C. e* ?* Z1 L9 q │ 10 - 5 - Clique Trees and VE (16-17).srt* w) M9 R3 u" |; Q+ L7 Z4 q6 U5 A
│ 10 - 6 - BP In Practice (15-38).mp4 w' N; d8 x! t! l; m% [
│ 10 - 6 - BP In Practice (15-38).srt% S4 S" }" ~6 H3 r+ f6 K# K
│ 10 - 7 - Loopy BP and Message Decoding (21-42).mp4$ V1 f7 b' Y" d: f4 e
│ 10 - 7 - Loopy BP and Message Decoding (21-42).srt9 x* f; D6 ~" K) S/ }: @
│ 11 - 1 - Max Sum Message Passing (20-27).mp4
5 Y9 _& `: }1 n7 K2 Z. k │ 11 - 1 - Max Sum Message Passing (20-27).srt* T0 E- S% r$ v: D
│ 11 - 2 - Finding a MAP Assignment (3-57).mp42 E# t' v% f8 D: K
│ 11 - 2 - Finding a MAP Assignment (3-57).srt
+ g" e5 g* H! K- t" v │ 12 - 1 - Tractable MAP Problems (15-04).mp4
% }: E: ~5 {$ [0 s; Z+ p │ 12 - 1 - Tractable MAP Problems (15-04).srt
* O$ t1 s+ |- ?' G' E) V8 A │ 13 - 1 - Simple Sampling (23-37).mp43 S9 q# q5 T) I7 }$ j
│ 13 - 1 - Simple Sampling (23-37).srt1 Y. d. Z' ^* M$ W% Q
│ 13 - 2 - Markov Chain Monte Carlo (14-18).mp4
! B$ r4 t! m o( h │ 13 - 2 - Markov Chain Monte Carlo (14-18).srt
a, A$ b; j$ U! P8 `+ A │ 13 - 3 - Using a Markov Chain (15-27).mp4
; c- @. |% m5 ^4 j5 F9 Y( ` │ 13 - 3 - Using a Markov Chain (15-27).srt
% R1 ]2 m! L, r9 _3 y2 g; V │ 13 - 4 - Gibbs Sampling (19-26).mp4
?! Q4 O+ I, c) U+ z8 y │ 13 - 4 - Gibbs Sampling (19-26).srt
" q; Q* m3 y& H9 n0 z- ?- { │ 13 - 5 - Metropolis Hastings Algorithm (27-06).mp4
# L( U+ @# t5 Z3 J2 _! ^ │ 13 - 5 - Metropolis Hastings Algorithm (27-06).srt; [) b/ F+ N0 l$ E
│ 14 - 1 - Inference in Temporal Models (19-43).mp4
) ]1 @9 i) L* ?% z5 F% Y% y │ 14 - 1 - Inference in Temporal Models (19-43).srt
* x) r# X& |- z0 S1 c0 R3 Z │ 14 - 2 - Inference- Summary (12-45).mp40 K9 d0 v0 |6 C
│ 14 - 2 - Inference- Summary (12-45).srt, D; D. L+ \( T
│ 15 - 1 - Maximum Expected Utility (25-57).mp4 p* ]; D8 Y. ?% o7 r, a$ S
│ 15 - 1 - Maximum Expected Utility (25-57).srt
6 t* s7 i _, X" m+ |8 w │ 15 - 2 - Utility Functions (18-15).mp4
. r- A- V* r3 F6 ^/ {& Q- U1 ~4 Q │ 15 - 2 - Utility Functions (18-15).srt6 x, d: Y# J0 P5 @$ G( ^, I, z8 Q p
│ 15 - 3 - Value of Perfect Information (17-14).mp41 i( P6 N- X; h
│ 15 - 3 - Value of Perfect Information (17-14).srt r( U0 J& M p; l, ?1 C! }) y
│ 16 - 2 - Regularization- Cost Function (10-10).mp4* @) Q* Q2 c$ Y/ H8 N
│ 16 - 2 - Regularization- Cost Function (10-10).srt/ W4 Q% m1 G/ y; A
│ 16 - 3 - Evaluating a Hypothesis (07-35).mp4
; t0 z3 W: d+ |+ A, b │ 16 - 3 - Evaluating a Hypothesis (07-35).srt
8 Q/ B! b+ O5 a; j8 v: j% c: u │ 16 - 5 - Diagnosing Bias vs Variance (07-42).mp4
1 l, H, b! U9 E │ 16 - 5 - Diagnosing Bias vs Variance (07-42).srt" g. s3 x- b6 ^! ]! R+ n W
│ 17 - 1 - Learning- Overview (15-35).mp4& E7 ~9 h" a, |# r" |& {* T
│ 17 - 1 - Learning- Overview (15-35).srt
" R: b+ @/ A0 H* { │ 18 - 1 - Maximum Likelihood Estimation (14-59).mp4
, R. u$ l! ?- |+ @- M' m/ o3 J │ 18 - 1 - Maximum Likelihood Estimation (14-59).srt$ R; x# @( D# c3 M' C
│ 18 - 3 - Bayesian Estimation (15-27).mp4( R7 ]7 q3 b! ?: Q" @* g
│ 18 - 3 - Bayesian Estimation (15-27).srt
/ N, u3 t7 B, N$ ]# w y3 g; \ │ 18 - 4 - Bayesian Prediction (13-40).mp4
0 F! ^* }: o8 j4 S │ 18 - 4 - Bayesian Prediction (13-40).srt \3 O& J$ Y6 b$ D7 u4 V: ]$ h
│ 2 - 1 - Semantics & Factorization (17-20).mp4
' s9 [1 q5 T% I9 h5 k │ 2 - 1 - Semantics & Factorization (17-20).srt [" a8 b Q8 N/ g
│ 2 - 2 - Reasoning Patterns (09-59).mp4- F/ c3 t* C+ G" X% m
│ 2 - 2 - Reasoning Patterns (09-59).srt' H) n* W1 j) f" [
│ 2 - 4 - Conditional Independence (12-38).mp4
+ r k) s$ Z- D$ `* z8 ` │ 2 - 4 - Conditional Independence (12-38).srt
; J7 Y% q* X, U9 ] │ 2 - 6 - Naive Bayes (09-52).mp4
8 n% ^! \: U! X+ U6 |( T& ? │ 2 - 6 - Naive Bayes (09-52).srt5 }" \1 l- G/ x7 E. X
│ 20 - 1 - Structure Learning Overview (5-49).mp4
" K6 F, ]9 H- o4 ?. \) P8 G │ 20 - 1 - Structure Learning Overview (5-49).srt) \( W1 Q# Z4 w5 y8 _* _
│ 20 - 2 - Likelihood Scores (16-49).mp44 i$ @ A) u! I7 S7 i
│ 20 - 2 - Likelihood Scores (16-49).srt, `; G- x1 d$ u5 V( U# ~9 i: [6 V
│ 20 - 4 - Bayesian Scores (20-35).mp4$ f& F. L O9 G, Y
│ 20 - 4 - Bayesian Scores (20-35).srt
( u" l, ?* R" q9 m8 ~ │ 21 - 3 - Analysis of EM Algorithm (11-32).mp42 \. U1 U# G- Q ^
│ 21 - 3 - Analysis of EM Algorithm (11-32).srt
8 U$ f' z3 |5 u │ 21 - 4 - EM in Practice (11-17).mp4
+ {+ c' Y" e2 {7 a; F" d │ 21 - 4 - EM in Practice (11-17).srt
+ T& q& J: T, z: ^. v9 @! E │ 21 - 5 - Latent Variables (22-00).mp4: m5 v. f8 B9 S4 _4 G$ ~
│ 21 - 5 - Latent Variables (22-00).srt
" \3 G# n' _. L; l5 k; Y$ ?- \1 b& M │ 22 - 1 - Summary- Learning (20-11).mp4 v. u$ {1 o k ^" d( ?
│ 22 - 1 - Summary- Learning (20-11).srt6 P9 D N& D# N
│ 23 - 1 - Class Summary (24-38).mp4& H' u0 H4 }- n% T# t* U" o
│ 23 - 1 - Class Summary (24-38).srt: _) e% k* a0 ?
│ 3 - 1 - Overview of Template Models (10-55).mp42 z) _5 r0 m6 B0 i6 ^. W+ w+ J; T. k
│ 3 - 1 - Overview of Template Models (10-55).srt
) M; c5 H3 S6 P. j1 p │ 3 - 2 - Temporal Models - DBNs (23-02).mp4
5 Y b: A6 u* f │ 3 - 2 - Temporal Models - DBNs (23-02).srt
( }' E3 T3 q( P5 W+ ~$ a( n# l │ 3 - 3 - Temporal Models - HMMs (12-01).mp4
( r% d, K$ N) N7 G0 @1 l │ 3 - 3 - Temporal Models - HMMs (12-01).srt; B6 U( G/ S$ |' O
│ 3 - 4 - Plate Models (20-08).mp42 Q' T* N% }& m' `, L8 Z1 L' k
│ 3 - 4 - Plate Models (20-08).srt
! ]# Q$ }9 J5 X& q" R4 W │ 4 - 1 - Basic Operations (13-59).mp42 _, R3 _% D0 \' R! e
│ 4 - 1 - Basic Operations (13-59).srt+ B( u3 G' s. R: R+ w
│ 4 - 2 - Moving Data Around (16-07).mp4
$ T$ \2 r1 _( ?" A1 o9 c │ 4 - 2 - Moving Data Around (16-07).srt3 a& t% J0 H6 W1 @5 {& w! F4 ]
│ 4 - 3 - Computing On Data (13-15).mp45 }: Z! e6 c+ z4 T: [& H
│ 4 - 3 - Computing On Data (13-15).srt
* l0 l& |% ^3 ]& n; l- { │ 4 - 4 - Plotting Data (09-38).mp4
+ f* l$ P9 I/ N) S1 s# Z │ 4 - 4 - Plotting Data (09-38).srt
/ f: W3 o& A0 i! I4 |) A │ 4 - 6 - Vectorization (13-48).mp40 K3 {, ~9 Q: w+ h/ R! i
│ 4 - 6 - Vectorization (13-48).srt! e5 @ ?$ j& ~8 i
│ 5 - 1 - Overview- Structured CPDs (08-00).mp4
! ~9 I5 b! y1 q( c │ 5 - 1 - Overview- Structured CPDs (08-00).srt& G* C% Q% m& n& C0 c; P6 h1 q
│ 5 - 2 - Tree-Structured CPDs (14-37).mp4' W5 R! \1 v6 l
│ 5 - 2 - Tree-Structured CPDs (14-37).srt
: D3 M! \# K+ _5 l T8 w6 s' ] │ 5 - 4 - Continuous Variables (13-25).mp4
- \7 {0 i! \4 O- \8 Y │ 5 - 4 - Continuous Variables (13-25).srt, Y6 P# d- D+ D$ D+ n; I" N
│ 6 - 1 - Pairwise Markov Networks (10-59).mp4
- e4 \% i4 Z' @" O: x0 q │ 6 - 1 - Pairwise Markov Networks (10-59).srt
2 F8 J/ o9 `+ V7 v/ d8 v; u$ J │ 6 - 2 - General Gibbs Distribution (15-52).mp4
% n) |' H- F: F& L* ]2 r3 T │ 6 - 2 - General Gibbs Distribution (15-52).srt
( A5 N0 E$ k$ f- Q7 ~! r% b │ 6 - 3 - Conditional Random Fields (22-22).mp43 s$ s: t# w0 V7 `: G
│ 6 - 3 - Conditional Random Fields (22-22).srt* z* }4 M. q: N; \3 p! h
│ 6 - 5 - I-maps and perfect maps (20-59).mp4
. b* k6 z" V$ B1 x. c │ 6 - 5 - I-maps and perfect maps (20-59).srt3 ]3 ?* R* |# X/ S1 d' d
│ 6 - 6 - Log-Linear Models (22-08).mp40 @0 o- x7 N6 X- P/ K
│ 6 - 6 - Log-Linear Models (22-08).srt* O, m, Q4 h+ n8 ?8 t' M
│ 7 - 1 - Knowledge Engineering (23-05).mp45 C( j6 m' o- g5 Z
│ 7 - 1 - Knowledge Engineering (23-05).srt; a1 {1 a( {) @3 W0 t" e! e
│ 8 - 2 - Overview- MAP Inference (09-42).mp4
; _6 h# n) V* v! ?7 B9 D* X │ 8 - 2 - Overview- MAP Inference (09-42).srt
; H; k! x! ^# k$ @8 V │ 8 - 3 - Variable Elimination Algorithm (16-17).mp4
4 I0 B' b& f) Q3 y1 V │ 8 - 3 - Variable Elimination Algorithm (16-17).srt. s) O; G8 N* z# Q1 P1 ^: C
│ 8 - 6 - Finding Elimination Orderings (11-58).mp4
: t) ?1 F* e7 P* u9 A9 J │ 8 - 6 - Finding Elimination Orderings (11-58).srt
$ h- T, l+ Z1 r2 k9 x │ 9 - 1 - Belief Propagation (21-21).mp4
" I. ?1 D% u2 y6 |3 L │ 9 - 1 - Belief Propagation (21-21).srt
h- T+ Y. a' M' P │ 9 - 2 - Properties of Cluster Graphs (15-00).mp46 @* d% O6 Q- N. R
│ 9 - 2 - Properties of Cluster Graphs (15-00).srt s. y$ H! X/ y
│ PGM-Programming_Assignment_1.zip
" Q* P: N, P o$ U# H N \ │ PGM-Programming_Assignment_2.zip0 z- O( C4 B/ d7 M- }5 V7 m5 s
│ PGM-Programming_Assignment_3.zip
8 Q7 V6 a( C; Y: @8 K4 z2 W │ PGM-Programming_Assignment_4.zip
% y; q) U, f# \ │ PGM-Programming_Assignment_5.zip5 t B8 n% O5 g) q# @
│ PGM-Programming_Assignment_6.zip' h1 P" P s: j# i4 U
│ PGM-Programming_Assignment_7.zip
# V2 v, M; D( Z* S5 \5 m( i9 g* j │ PGM-Programming_Assignment_8.zip0 y% }+ m/ H% O" n* f: _
│ PGM-Programming_Assignment_9.zip
+ Y- n1 z# P/ ?3 D- O5 b │ 下载说明.txt
3 z( Y4 A2 G* p! R6 a* q' o │ 关注我们.png0 ^, x& b3 G! K: Q( M# D4 @
│ 攻城狮论坛=网络技术+编程视频.url
1 o2 |" e; v1 p7 h$ \' I) o │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
# `9 }2 ?, \9 Y& g ? u │ 8 k. _0 n: F1 D& k
├─031_台大概率
/ L, \( i9 h. V% t │ └─031_台大概率/ p2 }5 ~! H; l8 `0 A W, M' Z5 a+ h7 _
│ ├─prob-001- }) I& J2 I5 j- B7 j {+ @* u
│ │ ├─01_Week_1& @( X+ R' v, N+ a$ S2 _; O
│ │ │ 01_1-0.mp4' O( g/ n+ p3 l# u$ b
│ │ │ 02_1-1_17-41.mp4
: ^ y* D) P& U: c' \6 H# u │ │ │ 02_1-1_17-41.pdf
- Q5 P$ _& l ]* t% {& I │ │ │ 02_1-1_17-41_0_.pdf
! u- N' m. D+ @6 f D/ H/ ^ │ │ │ 02_1-1_17-41_1_.pdf
. v7 ?2 F! P% f0 P8 ^ │ │ │ 02_1-1_17-41_2_.pdf$ H9 }9 }. A, B& o3 N2 G0 t& H' K: |
│ │ │ 03_1-2.a__11-46.mp4
/ B7 c3 a5 o2 a; y │ │ │ 04_1-2.b__09-40.mp4, \2 w$ F. N* |# _4 O
│ │ │ 05_1-3.a__11-24.mp4
9 X4 y5 x B1 k4 v- \( d │ │ │ 06_1-3.b__16-36.mp41 _. l1 U" E" `! {
│ │ │
6 F- W- B5 x2 a0 M1 x │ │ ├─02_Week_2
# r' q! Y; t" R1 \/ ~1 J │ │ │ 01_2-0_15-32.mp4, M/ V8 O& k. w0 v; E# p. M
│ │ │ 02_2-1.a__16-09.mp4
, L2 [8 \! E* b4 b │ │ │ 02_2-1.a__16-09.pdf
4 K1 \; C a* L │ │ │ 02_2-1.a__16-09_0_.pdf
, j, z( O( N) g$ w │ │ │ 02_2-1.a__16-09_1_.pdf
( M) ?' L# x9 o6 g2 g- M h$ n2 v │ │ │ 02_2-1.a__16-09_2_.pdf
" `7 t, ?0 B9 a │ │ │ 03_2-1.b__16-06.mp4
+ S0 C' v: m4 Y. W │ │ │ 04_2-1.c__10-07.mp4; R& d Z1 m; ~' P- m$ {6 x6 E9 g
│ │ │ 05_2-2.a__10-41.mp43 \! k. P" k, K- J
│ │ │ 06_2-2.b__12-43.mp4
" \- B4 a, P8 l0 d% u │ │ │ 07_2-2.c__16-02.mp4& I$ b# j% e7 P, e$ Q4 V8 c( ?
│ │ │
9 @: N6 l4 V9 n0 f: p, c │ │ ├─03_Week_32 U+ }' {6 n! a8 T9 {, s Z
│ │ │ 01_3-0_16-46.mp4
3 I7 Y5 H- w9 q; L7 k3 U- H2 q │ │ │ 02_3-1.a___09-12.mp44 }0 O- c1 X4 h" J
│ │ │ 02_3-1.a___09-12.pdf
0 f ~5 D! R; z9 G4 [) L8 D$ z2 N │ │ │ 02_3-1.a___09-12_0_.pdf
) N' P5 O% J& v2 Y3 A( Q! `8 c │ │ │ 02_3-1.a___09-12_1_.pdf
; y* a& u( o: p/ f5 q │ │ │ 02_3-1.a___09-12_2_.pdf+ J# r: h$ R6 \$ S1 g+ Z) S
│ │ │ 03_3-1.b__10-35.mp4
5 r2 O! N3 f- t9 V$ h" F; o9 A+ F │ │ │ 04_3-2_08-47.mp4
4 k% c( ]5 \0 R9 |# N │ │ │ 05_3-3.a__16-57.mp4) E; Z% Y# @6 \2 T: P
│ │ │ 06_3-3.b__12-58.mp4( R3 V: C3 S( l2 t
│ │ │ / T# z" e7 C! W G1 {; T* F
│ │ ├─04_Week_4# T# _0 x) g4 |
│ │ │ 01_4-0_17-33.mp4
' O" Y- D. W3 C X │ │ │ 02_4-1.a__13-53.mp4
, l' b: |- M( [: g │ │ │ 02_4-1.a__13-53.pdf
$ h) v# a) r0 O3 f │ │ │ 02_4-1.a__13-53_0_.pdf
, \/ f8 x. y& e9 J8 w L+ T+ ^6 q │ │ │ 02_4-1.a__13-53_1_.pdf
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: _6 i/ K7 v T, G% k │ │ │ 03_4-1.b__14-43.mp4
: X( F7 d) a D2 `9 } │ │ │ 04_4-1.c__5-18.mp4
: M4 s: b0 s2 N/ R │ │ │ 05_4-2.a_CDF__9-48.mp4
* u! r2 r3 g1 j+ x# ?4 N# e2 j │ │ │ 06_4-2.b_CDF__8-59.mp47 \! Y" @8 i }' m! y! P
│ │ │ 07_4-2.c_CDF__9-00.mp4! O" I' ~( m! P& W; ?* [
│ │ │ 08_4-3_PMF_11-26.mp4% S2 V, q8 U5 j9 v' ]. \
│ │ │ 09_4-4.a_I__14-41.mp4( K! y! h3 N' X
│ │ │ 10_4-4.b_I__8-47.mp4, r2 n2 D9 Q( o% F2 R4 u( ?# Q
│ │ │ ; f7 y: t1 Y/ j2 W9 H9 z) e6 C8 H
│ │ ├─05_Week_5
4 U$ h0 t% ^# C! F │ │ │ 01_5-0_14-09.mp4
, b; z* n2 |$ S$ J$ X │ │ │ 02_5-1.a_II__10-36.mp4
2 C0 J" @, U9 O │ │ │ 02_5-1.a_II__10-36.pdf
- Z5 y/ H8 }0 O3 x │ │ │ 02_5-1.a_II__10-36_0_.pdf$ s- H: B2 M8 a2 X
│ │ │ 02_5-1.a_II__10-36_1_.pdf" ^: R6 j# K* b: L8 ~+ f+ o( Z
│ │ │ 02_5-1.a_II__10-36_2_.pdf
4 ~+ V, i0 Y3 M& h$ L g │ │ │ 03_5-1.b_II__12-06.mp4" k& l6 Q) t8 v+ g; `9 K; j
│ │ │ 04_5-1.c_II__20-28.mp4
8 {- |2 U) W9 Q2 J' U │ │ │ 05_5-2_PDF_18-56.mp4
# h/ l8 O; Y3 o' l9 Q( m │ │ │ 06_5-3_I_18-12.mp4
& D5 ^" Y0 }& N2 v9 y" N │ │ │
0 p5 @2 X; K, l& E$ W( a; E │ │ ├─06_Week_6/ R; Z$ x5 c; [) O0 @2 Y
│ │ │ 01_6-0_10-13.mp43 d# I. }, R Z2 }4 a) c! H
│ │ │ 02_6-1.a_II__15-25.mp4
/ N- L) C5 x9 T4 o' K5 b4 j │ │ │ 02_6-1.a_II__15-25.pdf8 Q5 `! ~- \- i: Z( u- V6 k
│ │ │ 02_6-1.a_II__15-25_0_.pdf
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│ │ │ 03_6-1.b_II__16-08.mp4+ Y; a( G* r3 k; S. F/ ^/ b
│ │ │ 04_6-1.c_II__17-16.mp4
. e } Z& V! o g. r; | │ │ │ 05_6-1.d_II__5-40.mp4
3 a" b9 P4 d- X* m+ T1 ~ │ │ │ 06_6-2.a_I__16-35.mp4
* f# b2 g j s5 W. V4 \6 D/ M% m │ │ │ 07_6-2.b_I__10-41.mp4
X4 g0 y4 ]* Y0 t2 c │ │ │ 08_6-2.c_I__16-44.mp4/ Q8 G( D1 ~* s/ b9 J0 K
│ │ │ 09_6-2.d_I__14-30.mp4' I. {+ z8 K: M) X' \" D; f) {
│ │ │
- X5 E8 s; a3 Y1 j │ │ ├─07_Week_7# i3 a5 r( I+ z! q% m7 k2 e. K
│ │ │ 01_7-0.mp4
6 x2 B! w; [7 O/ ? │ │ │ 02_7-1.a_II__14-31.mp42 N' M9 r) m M8 R1 j
│ │ │ 02_7-1.a_II__14-31.pdf4 K( j7 L, ^- E' z6 |( r8 Q7 N
│ │ │ 02_7-1.a_II__14-31_0_.pdf
1 F, m' J$ K8 g │ │ │ 02_7-1.a_II__14-31_1_.pdf
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│ │ │ 03_7-1.b_II__13-07.mp4, y7 e8 g* h9 H5 `2 |4 N" w7 N! W
│ │ │ 04_7-2.a-___10-35.mp4# q% e/ V7 K0 r# V6 ^5 a0 T
│ │ │ 05_7-2.b-___08-42.mp42 z1 J" e1 Y2 V- j
│ │ │ 06_7-3.a-___15-07.mp4
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9 Y! C3 @4 C+ Y* n7 R7 ^ │ │ ├─08_Week_8. ?, E: J5 l6 T# \& \3 J
│ │ │ 01_8-0.mp42 N& t$ L8 W' z0 T7 L" U
│ │ │ 02_8-1.a__14-36.mp43 K6 P7 l+ K1 [/ `4 f4 E3 ]
│ │ │ 02_8-1.a__14-36.pdf: u) P1 ^ n, D& J4 Q
│ │ │ 02_8-1.a__14-36_0_.pdf# C& w) D) d. R/ p5 A/ |* W" \
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│ │ │ 02_8-1.a__14-36_2_.pdf
5 }& Y; A, J+ M │ │ │ 03_8-1.b__15-05.mp4
% F8 n: A* [/ r3 g │ │ │ 04_8-1.c__17-00.mp4
. v3 e) ~* U+ w$ x8 d2 C) U │ │ │ 05_8-1.d__11-18.mp43 G5 y# l5 E: V7 D0 f
│ │ │ 06_8-2_12-32.mp4
; r8 Q4 D1 B- ?8 f- G │ │ │ 07_8-3.a__10-06.mp4/ F/ I% F0 P" c2 [6 d A8 r
│ │ │ 08_8-3.b__17-27.mp44 H j/ ~% e- T
│ │ │ + Q$ }. o0 K! I# A
│ │ └─09_Week_94 D* e! B$ q) S+ w! j. w0 D2 x. n
│ │ 01_9-1.a__11-18.mp4
- J0 d" M+ d$ n │ │ 01_9-1.a__11-18.pdf
. N+ P: I5 H" @/ }8 X8 s. V# Y │ │ 01_9-1.a__11-18_0_.pdf
1 Z, y u5 I4 L% F7 s │ │ 01_9-1.a__11-18_1_.pdf
# a, E* x. Z) `- n# q5 C │ │ 01_9-1.a__11-18_2_.pdf
9 H7 E4 L' Z( R, k │ │ 02_9-1.b__13-49.mp4
2 b. o B% [' z5 Z │ │ 03_9-2.aMGF__10-17.mp47 s2 ~& s5 o) i9 E
│ │ 04_9-2.bMGF__14-06.mp4
' a; Y' G' L# O3 g3 P( o │ │ 05_9-2.cMGF__15-53.mp44 p0 G0 ]* S. m. R( d
│ │ 06_9-3.a__10-35.mp4
2 p; }- p, {* j( |( v │ │ 07_9-3.b__13-01.mp4
0 e5 f3 M) @7 t( {5 B Y │ │ 08_9-4.a-__16-45.mp4( G# L; E" Y" d2 t) C, U* h0 U
│ │ 09_9-4.b-__17-19.mp44 n$ [, e/ a# V& n) B n
│ │
3 [3 Y7 O* T0 O7 q! P# A │ └─概率-台大
& r( h5 o1 h/ w$ ]: w │ │ 02-8.R7 n8 F% Q7 l" \1 f: t4 `7 f. E% u' ~5 F
│ │ $ ^0 W8 r( N% O/ I1 o4 a
│ ├─1
2 Q3 z I9 ~* r │ │ Benson_Coursera_Week_1_簡.pdf! o: `3 Q, r4 b
│ │ Benson_Coursera_Week_1_繁.pdf
# P7 V; W6 ~: Y0 n7 H0 A% P │ │ Benson_Coursera_Week_1_繁空.pdf
+ p" Z) B( X# o │ │ 2 - 1 - 1-0：咱們先聊聊，這是門什麼樣的課呢-.mp4
( N: j; d: m& Q: W │ │ 2 - 2 - 1-1：機率概論 (17-41).mp4' P+ D% v4 c( p! |3 B# k
│ │ 2 - 3 - 1-2.a：集合論 (上) (11-46).mp4+ H. @. f0 c! c
│ │ 2 - 4 - 1-2.b：集合論 (下) (09-40).mp46 _7 V; N& z- ^, C- T
│ │ 2 - 5 - 1-3.a：機率名詞 (上) (11-24).mp4
6 v- k: a' v( _" s) f0 H │ │ 2 - 6 - 1-3.b：機率名詞 (下) (16-36).mp4$ F8 ?9 s, g0 B0 G+ A U' J
│ │
2 z6 ~" \, D* f$ W0 t0 t │ ├─2+ K( _* ]+ `% t5 F
│ │ Benson_Coursera_Week_2_簡.pdf( T" d6 j: y: A& [+ `1 ^
│ │ Benson_Coursera_Week_2_簡空.pdf. c6 ~+ }" }% m3 Y
│ │ Benson_Coursera_Week_2_繁.pdf! _3 Y1 w- Z; p: `. e9 I$ P
│ │ Benson_Coursera_Week_2_繁空.pdf+ K$ ~1 Y, P5 T; L& Y1 ?) J
│ │ 3 - 1 - 2-0：咱們聊聊，是學習，還是應付- (15-32).mp4' z c; J& _2 v
│ │ 3 - 2 - 2-1.a：機率公理性質 (上) (16-09).mp4
8 P* F2 I- ~; d │ │ 3 - 3 - 2-1.b：機率公理性質 (中) (16-06).mp40 |' }8 E, w7 G; U6 |( B
│ │ 3 - 4 - 2-1.c：機率公理性質 (下) (10-07).mp4
0 H) j' b( V: k3 W │ │ 3 - 5 - 2-2.a：條件機率 (上) (10-41).mp4
6 e n2 @1 Y& q$ ]: s U │ │ 3 - 6 - 2-2.b：條件機率 (中) (12-43).mp4$ ?8 Y K. |& g0 J! g$ f
│ │ 3 - 7 - 2-2.c：條件機率 (下) (16-02).mp4
# Y! m/ d4 H" ~( i7 ^ │ │
9 K: \6 r; Q6 E5 V5 h& P9 V │ ├─3
7 R# @+ c" k) k3 @% x8 X7 C │ │ Benson_Coursera_Week_3_簡.pdf
1 o5 }( K9 `6 Z$ e7 Q9 V2 a │ │ Benson_Coursera_Week_3_簡空.pdf6 e* Y7 x, R, |4 {
│ │ Benson_Coursera_Week_3_繁.pdf% m- }" | R9 p* z" K* h4 a
│ │ Benson_Coursera_Week_3_繁空.pdf! r$ \/ C4 g/ _* Q% [
│ │ 4 - 1 - 3-0：咱們聊聊，常見的錯誤？學習的關鍵？ (16-46).mp4
/ V% @! H& r2 E8 o2 B6 O. | │ │ 4 - 2 - 3-1.a：機率的獨立性 (上) (09-12).mp42 @; j8 [" q( U; \0 n: f
│ │ 4 - 3 - 3-1.b：機率的獨立性 (下) (10-35).mp40 [% z( R! Q( \; c; ^. h& {
│ │ 4 - 4 - 3-2：圖解繁複機率 (08-47).mp4
1 e& y+ ^, w6 \5 E: q │ │ 4 - 5 - 3-3.a：數數算機率 (上) (16-57).mp4
6 e2 Q( y* [; L% b0 r+ z% r' h │ │ 4 - 6 - 3-3.b：數數算機率 (下) (12-58).mp4
. Y- d; B* J; I. D* A │ │ |6 D* O+ G8 H9 g! d6 g' \
│ ├─47 t* T4 Y: J0 i/ \( j
│ │ Benson_Coursera_Week_4_簡.pdf# t4 ]8 t o$ B9 j
│ │ Benson_Coursera_Week_4_簡空.pdf
+ p5 | H2 ~* F4 V │ │ Benson_Coursera_Week_4_繁.pdf6 r$ z0 J8 G8 |* t. ^
│ │ Benson_Coursera_Week_4_繁空.pdf D" ^ p4 H6 J5 O% r4 G
│ │ 5 - 1 - 4-0：咱們聊聊，如何幫自己面對未來挑戰？ (17-33).mp4: I0 f( O# N. ^% ^* I. w9 @5 s
│ │ 5 - 10 - 4-4.b：離散機率分佈 I (下) (8-47).mp4
( z y5 n0 j) n! F+ W │ │ 5 - 2 - 4-1.a：隨機變數 (上) (13-53).mp4
& z. E) m/ e1 X │ │ 5 - 3 - 4-1.b：隨機變數 (中) (14-43).mp4
f. I# C2 z4 D" X# @& k │ │ 5 - 4 - 4-1.c：隨機變數 (下) (5-18).mp4" m+ f1 v9 K0 X9 U+ e# W+ B5 }
│ │ 5 - 5 - 4-2.a：累績分佈函數 CDF (上) (9-48).mp45 Q* F2 a9 \+ G X$ A' [
│ │ 5 - 6 - 4-2.b：累績分佈函數 CDF (中) (8-59).mp4. S I8 @$ K2 h) O1 I6 p+ m
│ │ 5 - 7 - 4-2.c：累績分佈函數 CDF (下) (9-00).mp4
8 J8 a/ ^ D8 _4 H K' D. f │ │ 5 - 8 - 4-3：機率質量函數 PMF (11-26).mp4) i( [. ^/ e" @8 Y5 R
│ │ 5 - 9 - 4-4.a：離散機率分佈 I (上) (14-41).mp46 Q; r, Y& M$ O) w# Z0 M1 ^6 c5 h9 C, _
│ │
. W7 [; w! _; I9 w, p- { │ ├─53 D" W Q$ I' C0 y3 z6 I
│ │ Benson_Coursera_Week_5_簡.pdf
# D7 B5 E3 w* n │ │ Benson_Coursera_Week_5_簡空.pdf
7 j- N( S8 F& k │ │ Benson_Coursera_Week_5_繁.pdf
# k' p+ J& ~5 g9 h( q) Z, } │ │ Benson_Coursera_Week_5_繁空.pdf+ r4 m1 A5 L" P6 X# P
│ │ 6 - 1 - 5-0：咱們聊聊，願你夜夜好眠到天明！ (14-09).mp4
/ B6 k* S- C. A3 N │ │ 6 - 2 - 5-1.a：離散機率分佈 II (上) (10-36).mp4
% l7 Z1 a/ ]$ D2 g │ │ 6 - 3 - 5-1.b：離散機率分佈 II (中) (12-06).mp4
; y! r8 e" o6 ?8 a7 Y& K v/ }+ u2 m │ │ 6 - 4 - 5-1.c：離散機率分佈 II (下) (20-28).mp4
$ R( ?2 ]: E- x │ │ 6 - 5 - 5-2：機率密度函數 PDF (18-56).mp4; n7 e7 b0 U# f, L5 l/ ]' T6 T+ }
│ │ 6 - 6 - 5-3：連續機率分佈 I (18-12).mp4& o4 p- s5 |+ D6 s" B( {) A
│ │
5 T, K9 y3 L" A2 e5 [( |2 C! E │ ├─6
" N* K. D* T6 W8 Y) A │ │ Benson_Coursera_Week_6_簡.pdf
" n/ t% R% _9 ]4 W8 @/ J │ │ Benson_Coursera_Week_6_簡空.pdf
5 Y, M/ {: W0 o% t │ │ Benson_Coursera_Week_6_繁.pdf9 _% l. \7 m6 H
│ │ Benson_Coursera_Week_6_繁空.pdf8 M' j8 c( |/ W% D4 p, i
│ │ 7 - 1 - 6-0：咱們聊聊，成功者的條件是什麼？ (10-13).mp4
9 f7 V! i* n! n0 T$ ?) ~) G; g │ │ 7 - 2 - 6-1.a：連續機率分佈 II (上) (15-25).mp4
1 A" `3 U7 W! ]4 } │ │ 7 - 3 - 6-1.b：連續機率分佈 II (中) (16-08).mp4
" }+ S& p2 d$ ?5 B5 g2 _. \ │ │ 7 - 4 - 6-1.c：連續機率分佈 II (下) (17-16).mp4
& J7 t$ e: u# A' }$ n │ │ 7 - 5 - 6-1.d：連續機率分佈 II (末) (5-40).mp4
5 X2 x. [7 ]1 v │ │ 7 - 6 - 6-2.a：期望值 I (上) (16-35).mp4
: L4 R9 M# L0 C& @2 s │ │ 7 - 7 - 6-2.b：期望值 I (中) (10-41).mp4
5 y) k) h+ t1 e% Q! L* U/ I │ │ 7 - 8 - 6-2.c：期望值 I (下) (16-44).mp46 b; N) h; `$ p$ V& r
│ │ 7 - 9 - 6-2.d：期望值 I (末) (14-30).mp4
( f; M' R# v' N/ f# U6 K │ │
, W, S% f! x4 z, l- y. i9 R' e │ ├─73 }: T2 r) f* {( Z5 f
│ │ Benson_Coursera_Week_7_簡.pdf. W* Y% c% u( C5 g
│ │ Benson_Coursera_Week_7_簡空.pdf# y* f+ M/ W4 W. g& E
│ │ Benson_Coursera_Week_7_繁.pdf
# o0 J1 i/ N1 c- [" r │ │ Benson_Coursera_Week_7_繁空.pdf( [7 A L5 K" U- X
│ │ 8 - 1 - 7-0：咱們聊聊，每天都在忙，忙的有用嗎？.mp4
0 p' D- d' i/ _3 a │ │ 8 - 2 - 7-1.a：期望值 II (上) (14-31).mp49 y. q3 O& l+ J, H1 \# O
│ │ 8 - 3 - 7-1.b：期望值 II (下) (13-07).mp4% l1 T8 G( \5 R8 h, w4 k& l
│ │ 8 - 4 - 7-2.a- 隨機變數之函數 (上) (10-35).mp49 Q; E' v7 l" {
│ │ 8 - 5 - 7-2.b- 隨機變數之函數 (下) (08-42).mp4
1 U" p% q2 O8 [3 F/ N4 l# J! m/ }$ { │ │ 8 - 6 - 7-3.a- 條件機率分佈與失憶性 (上) (15-07).mp4
4 V( J* a; x/ M* O6 w, k │ │ 8 - 7 - 7-3b- 條件機率分佈與失憶性 (下) (19-20).mp4
; L! o& V m* n- Q │ │ ( E* Q, y$ G8 N# h$ p" K
│ ├─82 V; B ]+ `! E: i7 S J, N7 q5 M
│ │ Benson_Coursera_Week_8_簡.pdf3 J; m8 r! t# v! a, s7 `. P. Q
│ │ Benson_Coursera_Week_8_簡空.pdf; I | F# N& p2 K& o3 c
│ │ Benson_Coursera_Week_8_繁.pdf
. b7 |, x& A5 _* h& o' S1 M3 {! A& Y │ │ Benson_Coursera_Week_8_繁空.pdf
/ b. f* O5 p0 o. C8 \- N) | │ │ 9 - 1 - 8-0：咱們聊聊，如何探索有意義的人生？.mp4
# `. y3 g! o$ ` x& d- @/ k │ │ 9 - 2 - 8-1.a：聯合機率分佈 (上) (14-36).mp47 v+ g2 }3 l# b! o& m! U1 T$ Z( g
│ │ 9 - 3 - 8-1.b：聯合機率分佈 (中) (15-05).mp4
% \1 e- ^ D7 p/ w │ │ 9 - 4 - 8-1.c：聯合機率分佈 (下) (17-00).mp4, E% y7 g6 ~4 U* a+ u% B" V# |8 w
│ │ 9 - 5 - 8-1.d：聯合機率分佈 (末) (11-18).mp4+ N- g- T, c+ v" s
│ │ 9 - 6 - 8-2：邊際機率分佈 (12-32).mp4- O5 { g9 a& `3 q2 ?9 z7 K# ~) O, q" o6 x
│ │ 9 - 7 - 8-3.a：雙變數期望值 (上) (10-06).mp4- g. S' }, u% h
│ │ 9 - 8 - 8-3.b：雙變數期望值 (下) (17-27).mp4" m9 k4 \3 H% T4 m
│ │ 2 y& ?& a3 \' g* S& W$ J" M
│ └─9* P, Z8 G u* B
│ Benson_Coursera_Week_9_簡.pdf' H) e2 `7 l9 x$ ?& D0 B
│ Benson_Coursera_Week_9_簡空.pdf F* ` Y# B2 j
│ Benson_Coursera_Week_9_繁.pdf; O X9 S- G/ F" R7 s" e
│ Benson_Coursera_Week_9_繁空.pdf
/ r$ h, O" e3 ~) U/ o" _: j/ a( Y$ i │ 10 - 1 - 9-1.a：隨機變數之和 (上) (11-18).mp4
2 K) @, ?1 g, Y( q* P5 q$ { j; | │ 10 - 2 - 9-1.b：隨機變數之和 (下) (13-49).mp4
& A+ K1 C1 }- D o1 ? │ 10 - 3 - 9-2.a：MGF (上) (10-17).mp47 j L% i" Y: u' B. W* c, V
│ 10 - 4 - 9-2.b：MGF (中) (14-06).mp4) h" H0 K2 `. A9 y# r9 |+ Q, _
│ 10 - 5 - 9-2.c：MGF (下) (15-53).mp42 r6 @. `) D8 ]% o0 ~- v$ E( Q$ H
│ 10 - 6 - 9-3.a：多個隨機變數和 (上) (10-35).mp4
! O3 o. o, y) Z! L │ 10 - 7 - 9-3.b：多個隨機變數和 (下) (13-01).mp46 d* Y( W7 ]. e- N5 G5 p- A
│ 10 - 8 - 9-4.a：中央極限定理-萬佛朝宗 (上) (16-45).mp4: }$ d# _+ w8 ]. x
│ 10 - 9 - 9-4.b：中央極限定理-萬佛朝宗 (下) (17-19).mp4
" _9 G2 B' h5 E: \! k$ ~$ U( D │
9 u5 h' G* J/ k! [7 ?" \ ├─036_Coding the Matrix1 K6 S5 v4 n& N
│ └─036_Coding the Matrix: Z0 G+ z( k7 X+ n
│ └─matrix-002
* M2 H" i6 c) e1 V* [ │ │ 下载说明.txt. `* `( F% Q2 p/ Y# V+ X* t* u4 t# s1 |
│ │ 关注我们.png" n5 f f! I- q. s$ z- {& x3 `
│ │ 攻城狮论坛=网络技术+编程视频.url
1 d4 o9 [* Q' I( q# @+ Q │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt
8 H) L4 p, \/ v! x* ] │ │
9 y+ [+ p9 t6 n5 { │ ├─01_Week_0-_The_Function_and_the_Field C; `! R% A' A8 d9 z- D8 P3 {
│ │ 01_Course_Introduction_Part_1__9-53.mp4
5 N4 t7 O- l. K8 x- H0 {, o │ │ 01_Course_Introduction_Part_1__9-53.srt
3 c6 r: F0 w1 Q3 M/ s/ V │ │ 01_Course_Introduction_Part_1__9-53.txt
( l' H; D* ^$ i3 O! D │ │ 02_Course_Introduction_Part_2__8-49.mp4# U: L' A- t, Q6 S( d
│ │ 02_Course_Introduction_Part_2__8-49.srt
, S7 Q& I6 r: Q2 V │ │ 02_Course_Introduction_Part_2__8-49.txt
h+ `3 C2 d+ R/ B2 q1 a: V │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.mp4
9 Q* r( F( T% B+ x3 o& T% k │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.pdf- ^6 `8 e9 M. K, n' Y
│ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.srt
+ l1 M4 u/ X4 C1 V) q) r │ │ 03_The_Function-_The_function_and_other_preliminaries__20-55.txt4 X# D' G/ D$ p
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.mp4
2 m6 x+ I# A) ]* U │ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.pdf/ [4 y/ B& t" Q9 Q' Q1 w) k* m
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.srt/ g: `+ E$ K/ ] Z4 \, {1 X, ~
│ │ 04_The_Field-_Introduction_to_complex_numbers__5-52.txt: E7 `9 D. O+ W% R0 I+ M
│ │ 05_The_Field-_Playing_with_C_15-19.mp4
" n8 l2 B6 X6 [' r0 a │ │ 05_The_Field-_Playing_with_C_15-19.pdf
/ m. b7 ^' O% \: P2 w/ d( a │ │ 05_The_Field-_Playing_with_C_15-19.srt
* p( W q: j! X2 q* z/ ^ │ │ 05_The_Field-_Playing_with_C_15-19.txt# ~/ t: C* G: p1 k5 L
│ │ 06_The_Field-_Playing_with_GF2__10-28.mp48 k( T; z# s( A- ~
│ │ 06_The_Field-_Playing_with_GF2__10-28.pdf
2 q) s! h$ m8 W( o/ z. b3 J │ │ 06_The_Field-_Playing_with_GF2__10-28.srt2 A7 K( A7 V* r" J
│ │ 06_The_Field-_Playing_with_GF2__10-28.txt5 m X+ j& G+ f$ ]) U
│ │ - S0 `% s7 z5 m
│ ├─02_Week_1-_The_Vector
. z/ f! _! _2 ? k1 q$ T9 U7 _; ~ │ │ 01_The_Vector-_What_is_a_vector__8-20.mp41 z( R. T; k+ e0 D
│ │ 01_The_Vector-_What_is_a_vector__8-20.pdf
7 x) S$ v* r: ^4 V0 p, H │ │ 01_The_Vector-_What_is_a_vector__8-20.srt9 r) T1 R j& v
│ │ 01_The_Vector-_What_is_a_vector__8-20.txt. X+ k5 B( S, M3 R5 c, {4 J
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.mp4
9 g: @) Q, K' r' j │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.pdf0 O0 K+ b+ j' F$ Y9 _% X B) ^/ v
│ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.srt
) F& k6 _& ~ d0 k; D! ]3 y │ │ 02_The_Vector-_Vector_addition_and_scalar-vector_multiplication__10-16.txt$ G; R2 j% F- p. g! Y5 D, c
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.mp46 O, {3 K3 O$ ^/ k" j
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.pdf! o4 ~' q# [! j) @ s' o' [
│ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.srt
$ \+ Z/ N" F- n7 I& b/ I │ │ 03_The_Vector-_Dictionary-based_representations_of_vectors__9-10.txt# G5 i# O8 I8 E. P e' a& R
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.mp4
0 J& y( a: ]- E │ │ 04_The_Vector-_Vectors_over_GF2__9-18.pdf
2 M1 a/ E* P7 _. t │ │ 04_The_Vector-_Vectors_over_GF2__9-18.srt2 }2 `) E$ h3 J& w
│ │ 04_The_Vector-_Vectors_over_GF2__9-18.txt
2 z- A) I" `! I. L5 H4 G │ │ 05_The_Vector-_Dot-product__8-49.mp4
; R0 Q, {3 I) e" G0 b │ │ 05_The_Vector-_Dot-product__8-49.pdf
% @4 q& a6 p4 u1 a │ │ 05_The_Vector-_Dot-product__8-49.srt
5 w- }9 J1 D/ e" U │ │ 05_The_Vector-_Dot-product__8-49.txt( j+ `7 ]4 A8 K/ Y( z
│ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.mp4
# X* w+ A1 z0 I! {7 G+ z# x │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.pdf
7 v) U1 n2 n [" f │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.srt
" {7 b' B* I4 S- P │ │ 06_The_Vector-_Dot-product_of_vectors_over_GF2__4-44.txt
! h8 `* S; O& W7 @9 l$ U5 G │ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.mp4
1 R$ ~9 @: x* O │ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.pdf V) b0 H4 }. E; U/ f; s
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.srt. `0 k' t& |1 J5 d. m* S
│ │ 07_The_Vector-_Solving_a_triangular_system_of_linear_equations__4-00.txt
2 D4 T& u7 P- {* V │ │
) f' @; W$ w6 [2 m │ ├─03_Week_2-_The_Vector_Space
6 A& h9 G- r1 k1 [ │ │ 01_The_Vector_Space-_Linear_combinations.mp4( q, Q& t' S" l% P4 F7 t
│ │ 01_The_Vector_Space-_Linear_combinations.pdf) T+ x) D5 J) O( g3 s& ] ^
│ │ 01_The_Vector_Space-_Linear_combinations.srt& {7 }2 P5 {# j! f5 s9 \7 {
│ │ 01_The_Vector_Space-_Linear_combinations.txt$ h; _' p/ {) ^: O' j4 Y. Y
│ │ 02_The_Vector_Space-_Span.mp4$ Q' x2 c: N) z
│ │ 02_The_Vector_Space-_Span.pdf4 c, H3 @: Q7 l% U& n' G
│ │ 02_The_Vector_Space-_Span.srt
2 i" X7 ]4 H; i: v │ │ 02_The_Vector_Space-_Span.txt1 d- R: e/ Z7 n! ?7 }" j# ~
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.mp4
d; k; Q5 W, T! h( T' \+ y/ G2 S │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.pdf
5 }# O9 V' X1 \$ } \+ ~ │ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.srt* _/ m0 t- s6 n* d( E$ f& j5 p
│ │ 03_The_Vector_Space-_Geometry_of_Sets_of_Vectors.txt
2 d- D( y- A: X3 c/ X) } │ │ 04_The_Vector_Space-_Vector_spaces.mp4) _. G4 d2 h$ p+ V2 ~7 V
│ │ 04_The_Vector_Space-_Vector_spaces.pdf
$ X8 |. Y n# j6 W0 r; N │ │ 04_The_Vector_Space-_Vector_spaces.srt6 u7 E( U/ s; m0 A" A3 A8 q) u
│ │ 04_The_Vector_Space-_Vector_spaces.txt
0 t* I; j6 e1 S9 H5 G8 A0 ^ │ │ 05_The_Vector_Space-_Checksum_function.mp4: W% ]$ d# x1 B! K5 s. u- ]" ~
│ │ 05_The_Vector_Space-_Checksum_function.pdf7 L* `0 a) Q7 b$ b. Y: t' n4 t
│ │ 05_The_Vector_Space-_Checksum_function.srt z9 }5 t& U1 O) ?/ j
│ │ 05_The_Vector_Space-_Checksum_function.txt5 v8 u7 |4 L% V4 F5 r
│ │ / m+ z+ }) w+ `$ T m9 \% A) F
│ ├─04_Week_3-_The_Matrix
, L& c9 Q) \7 P$ |1 q0 L% F* _3 [ │ │ 01_The_Matrix-_What_is_a_matrix.mp4
7 _; o) ~$ g) u+ i! S# ?$ Y9 u* V │ │ 01_The_Matrix-_What_is_a_matrix.pdf
9 ?, ` W% [8 w% G │ │ 01_The_Matrix-_What_is_a_matrix.srt
, R5 m- k' Q' I6 d+ Z │ │ 01_The_Matrix-_What_is_a_matrix.txt0 M+ q0 r2 y8 T! Y
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.mp4
3 N% z0 l7 V) \6 V0 ]/ y │ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.pdf p8 i/ [' d7 j/ O! O; J( O& k
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.srt" l7 |- W! ]! P0 f
│ │ 02_The_Matrix-_Matrix-vector_and_vector-matrix_multiplication.txt* p: ]5 a! Q' g& |
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.mp4
: l) Z0 F) _4 m5 A3 s │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.pdf8 i; Y4 l( C) h5 M! R# y( W1 L
│ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.srt
% S6 U' g% t9 R8 q$ w │ │ 03_The_Matrix-_Matrix-vector_multiplication_in_terms_of_dot-products.txt
8 ?, v' f0 a& O* L1 @ │ │ 04_The_Matrix-_Null_space.mp4
1 ^8 L! S7 ]0 X1 p$ s │ │ 04_The_Matrix-_Null_space.pdf& u4 g) t1 \" f, m5 F7 x' r
│ │ 04_The_Matrix-_Null_space.srt7 \! |0 y- v+ X
│ │ 04_The_Matrix-_Null_space.txt
% _* `4 z, ]- {1 p3 j9 G! [8 S │ │ 05_The_Matrix-_Error-correcting_codes.mp4
' I% u; g0 M7 U │ │ 05_The_Matrix-_Error-correcting_codes.pdf
4 F) q9 c% ]3 n/ e( e" h- Z │ │ 05_The_Matrix-_Error-correcting_codes.srt
- [. ^: s2 ^, x# _5 d │ │ 05_The_Matrix-_Error-correcting_codes.txt
( u( r$ n, f5 j5 O( U │ │ 06_The_Matrix-_Matrices_and_their_functions.mp4
' k5 M8 w. e) W+ f │ │ 06_The_Matrix-_Matrices_and_their_functions.pdf
% _ Z- v4 G& E6 g" ^( | │ │ 06_The_Matrix-_Matrices_and_their_functions.srt; [/ o/ I" |; B. Y0 C1 D2 |5 u3 [
│ │ 06_The_Matrix-_Matrices_and_their_functions.txt' D% c2 G# X/ e$ Z& M; O
│ │ 07_The_Matrix-_Linear_functions.mp4
; |2 ]7 z3 E" T+ d Y │ │ 07_The_Matrix-_Linear_functions.pdf
4 K# a$ l- U% k4 V! Y q( g │ │ 07_The_Matrix-_Linear_functions.srt# |. P1 v: X3 ?! j; m
│ │ 07_The_Matrix-_Linear_functions.txt" P( O" O; K2 Z8 V* [4 G' e7 o
│ │ 08_The_Matrix-_Matrix-matrix_multiplication.mp4
0 m" t1 F( V' J. T4 E3 e │ │ 08_The_Matrix-_Matrix-matrix_multiplication.pdf
: T \8 a9 R$ P& W7 Y- k* Z │ │ 08_The_Matrix-_Matrix-matrix_multiplication.srt
# [) Z1 X# E1 {3 g2 F9 w │ │ 08_The_Matrix-_Matrix-matrix_multiplication.txt# O+ j0 ^% e- n
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.mp44 a& A6 n0 V) ]' ^+ E
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.pdf2 N( u. b' Y% E/ ~
│ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.srt
7 G6 [7 n( m* d$ p) @4 J- } │ │ 09_The_Matrix-_Matrix-matrix_multiplication_and_function_composition.txt& o# ^6 X0 D2 a7 K7 {
│ │ 10_The_Matrix-_Matrix_inverse.mp4
3 l- D1 Q6 J) c' ]/ A& U1 {% y │ │ 10_The_Matrix-_Matrix_inverse.pdf
$ G9 d* j& C! M; `; P, B$ @ │ │ 10_The_Matrix-_Matrix_inverse.srt+ }- X6 V, F; r* P5 c
│ │ 10_The_Matrix-_Matrix_inverse.txt
3 V9 \ A [0 ]! x │ │ $ A" _ H) q3 W) z) x9 B
│ ├─05_Week_4-_The_Basis
' e' E; T2 X8 e! z) w │ │ 01_The_Basis-_Coordinate_systems.mp4
& X5 W1 O$ C) b+ n' U │ │ 01_The_Basis-_Coordinate_systems.pdf5 h# `% d0 Y0 `- r
│ │ 01_The_Basis-_Coordinate_systems.srt6 ^7 }% S; T& Y1 C
│ │ 01_The_Basis-_Coordinate_systems.txt
3 C8 c3 O* l- k │ │ 02_The_Basis-_Lossy_compression.mp4
- h _- q" I x% f │ │ 02_The_Basis-_Lossy_compression.pdf
% E* A( n5 L: h4 z) J │ │ 02_The_Basis-_Lossy_compression.srt
; K0 [, G# a6 ^ │ │ 02_The_Basis-_Lossy_compression.txt
4 }' C: w$ B3 M1 X2 A │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.mp4
, K! h0 g! y0 k │ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.pdf, b$ Z) s# i- `' [& t
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.srt4 |7 E E( b3 L0 T" | q- ?2 B- d
│ │ 03_The_Basis-_Algorithms_for_finding_a_set_of_generators.txt
. a( E, ] p0 Y4 ]+ `3 X( n2 ~3 m │ │ 04_The_Basis-_Minimum_spanning_forest.mp48 @- y6 o5 ~0 e
│ │ 04_The_Basis-_Minimum_spanning_forest.pdf9 x7 ?, J, _) }! [% {
│ │ 04_The_Basis-_Minimum_spanning_forest.srt3 f) [5 B. u/ L; U' t! h( |& N
│ │ 04_The_Basis-_Minimum_spanning_forest.txt
+ T, `" P, f6 J4 } P# I │ │ 05_The_Basis-_Linear_dependence.mp4+ ]; i% `# P7 N2 L8 N: s
│ │ 05_The_Basis-_Linear_dependence.pdf! Y, ?# ~1 C5 _7 `
│ │ 05_The_Basis-_Linear_dependence.srt
) H1 X9 {9 V2 v3 n$ I! _ │ │ 05_The_Basis-_Linear_dependence.txt
& l) q6 c3 t; _% p7 u# g8 T6 F# Y │ │ 06_The_Basis-_Basis.mp49 l- B+ c3 H4 d+ d; y; o3 e2 C
│ │ 06_The_Basis-_Basis.pdf7 `3 x2 F$ I! b- {8 c/ G5 a- j
│ │ 06_The_Basis-_Basis.srt
, X* W. Q5 L; e9 F% X, o& { w │ │ 06_The_Basis-_Basis.txt
+ V, y$ m4 L6 L │ │ 07_The_Basis-_Unique_representation.mp4
$ M- D6 F. A# R. R, ~( x3 C: g4 \ │ │ 07_The_Basis-_Unique_representation.pdf
! n$ k9 r) h1 k1 \ │ │ 07_The_Basis-_Unique_representation.srt9 C4 t4 K2 w2 J, z" a0 L/ P: T
│ │ 07_The_Basis-_Unique_representation.txt
/ N2 g, S0 }: o" W' k: h# | │ │ 08_The_Basis-_Change_of_basis.mp4; ~5 _/ x! M E- O; j7 J
│ │ 08_The_Basis-_Change_of_basis.pdf. }; K3 f# P: f4 L1 E' W8 @
│ │ 08_The_Basis-_Change_of_basis.srt
; S- ^9 O7 T$ O% [9 ^/ n │ │ 08_The_Basis-_Change_of_basis.txt, z) E. m w" {4 f5 Z
│ │ 09_The_Basis-_Perspective_rendering.mp43 c5 z6 P3 i, Z. [; L/ c
│ │ 09_The_Basis-_Perspective_rendering.pdf
5 ~& q1 I/ a L0 a │ │ 09_The_Basis-_Perspective_rendering.srt
9 v. x9 q( m1 {0 L( z: Q. w1 |& d │ │ 09_The_Basis-_Perspective_rendering.txt
% H5 c" G) @+ e( S& d │ │ 10_The_Basis-_Perspective_rectification.mp4
" d3 z1 g, p d. p& t" c │ │ 10_The_Basis-_Perspective_rectification.pdf b: K; C1 [4 Y3 F5 ]; ?
│ │ 10_The_Basis-_Perspective_rectification.srt
: t% E8 z/ y4 y) f$ i+ X3 Y- b │ │ 10_The_Basis-_Perspective_rectification.txt
- Y' D9 C9 ~$ J# h7 u │ │ 11_The_Basis-_The_Exchange_Lemma.mp4
3 m( D. @4 H; i$ q0 M │ │ 11_The_Basis-_The_Exchange_Lemma.pdf
f9 [/ M. `* m7 [% O% I │ │ 11_The_Basis-_The_Exchange_Lemma.srt7 K+ e. y2 \4 s* G2 H
│ │ 11_The_Basis-_The_Exchange_Lemma.txt
5 k: F/ [3 G. Z6 ^' n7 R; X │ │ / O, n0 f+ B5 `' e* N( _
│ ├─06_Week_5-_Dimension
# I" s! V3 i" [0 `* x5 n4 K │ │ 01_Dimension-_The_size_of_a_basis.mp4
4 G* e3 H: h$ u. B. R( H │ │ 01_Dimension-_The_size_of_a_basis.pdf5 W& [; V6 C L3 F/ u
│ │ 01_Dimension-_The_size_of_a_basis.srt. T0 K. k: a1 W9 W& ?: f) e, L
│ │ 01_Dimension-_The_size_of_a_basis.txt: W6 n. {) @( Q' d; `
│ │ 02_Dimension-_Dimension_and_rank_I.mp4% ^" J. E# v! K0 F" g
│ │ 02_Dimension-_Dimension_and_rank_I.pdf1 G& X, N; Y. d( m3 i6 {9 X r
│ │ 02_Dimension-_Dimension_and_rank_I.srt
' y1 P! a ]6 B( S/ a9 P │ │ 02_Dimension-_Dimension_and_rank_I.txt
1 Z$ `3 c1 w; Z% T │ │ 03_Dimension-_Dimension_and_rank_II.mp4
4 u. _0 @ h1 R7 H+ L6 K4 z │ │ 03_Dimension-_Dimension_and_rank_II.pdf0 q; m* u# V! q9 k+ q
│ │ 03_Dimension-_Dimension_and_rank_II.srt9 z, J+ ]5 X! L* O
│ │ 03_Dimension-_Dimension_and_rank_II.txt
5 r7 J- F! D g; b │ │ 04_Dimension-_Direct_sum.mp4. w: u* s; P+ M U5 \
│ │ 04_Dimension-_Direct_sum.pdf
+ s+ P% A$ y7 L9 E& y. x8 ] │ │ 04_Dimension-_Direct_sum.srt
' V7 A2 Z4 s! V8 k3 l, W% f │ │ 04_Dimension-_Direct_sum.txt
9 o: m, U& n+ f" H1 M8 y │ │ 05_Dimension-_Dimension_and_linear_functions_I.mp4' Q1 H3 R5 K7 X/ X8 f+ ^/ W
│ │ 05_Dimension-_Dimension_and_linear_functions_I.pdf
4 @6 _4 R1 x+ H4 U, o+ t │ │ 05_Dimension-_Dimension_and_linear_functions_I.srt
T8 r/ \' t' r, a │ │ 05_Dimension-_Dimension_and_linear_functions_I.txt
; j4 I: P' m- b │ │ 06_Dimension-_Dimension_and_linear_functions_II.mp4
: x7 a% H/ k6 k1 P │ │ 06_Dimension-_Dimension_and_linear_functions_II.pdf5 _- J, S1 I" {3 {2 F& b- k
│ │ 06_Dimension-_Dimension_and_linear_functions_II.srt9 _/ a. t. Q: F& `
│ │ 06_Dimension-_Dimension_and_linear_functions_II.txt; l# x( Q# ^* {/ p1 R0 j
│ │ 07_Dimension-_Two_representations_of_vector_spaces.mp4
" `8 G5 H5 ~0 ^4 i │ │ 07_Dimension-_Two_representations_of_vector_spaces.pdf4 V, X8 f3 |' n' x
│ │ 07_Dimension-_Two_representations_of_vector_spaces.srt( Y$ Z0 G9 E. o5 f, o& o* K
│ │ 07_Dimension-_Two_representations_of_vector_spaces.txt
- ]: T: ?4 I7 A+ P% Q │ │ 08_Dimension-_Threshold_secret_sharing.mp4+ l- s' o# F9 a2 {1 _* ]1 f
│ │ 08_Dimension-_Threshold_secret_sharing.pdf3 ?6 P0 U" ~& i6 T) d$ q5 P( I
│ │ 08_Dimension-_Threshold_secret_sharing.srt
3 O% e8 k8 g& A4 H6 k7 Q1 m& B │ │ 08_Dimension-_Threshold_secret_sharing.txt6 A3 ]$ o0 p; Q6 K$ v: m1 E& x
│ │ 6 V4 ]7 D% x9 L
│ ├─07_Week_6-_Gaussian_Elimination_and_the_Inner_Product
) {9 a- h9 Q2 ^8 i │ │ 01_Gaussian_Elimination-_Echelon_form.mp4
6 y5 `$ d' v4 x" y, R- A7 T │ │ 01_Gaussian_Elimination-_Echelon_form.pdf
9 a' D X; I, d1 n' [ │ │ 01_Gaussian_Elimination-_Echelon_form.srt
# {, O( ^) `( k3 G! v$ o │ │ 01_Gaussian_Elimination-_Echelon_form.txt) M/ |) ^1 n( N+ n! }
│ │ 04_Gaussian_Elimination-_Factoring_integers.mp4' R9 s) ^2 k. G* X
│ │ 04_Gaussian_Elimination-_Factoring_integers.pdf
, h5 _+ b& l6 G0 A! t& u/ o │ │ 04_Gaussian_Elimination-_Factoring_integers.srt
) t8 x4 H: y* E │ │ 04_Gaussian_Elimination-_Factoring_integers.txt
' v" j3 J7 J: r- W4 d' u9 p' s, Z │ │ 05_The_Inner_Product-_The_inner_product.mp4
" U/ S5 l0 w6 L6 ^1 ? w │ │ 05_The_Inner_Product-_The_inner_product.pdf
- ?) R* V5 C1 f( ] │ │ 05_The_Inner_Product-_The_inner_product.srt5 ]7 j- z' T/ N8 K
│ │ 05_The_Inner_Product-_The_inner_product.txt9 k; @3 Q% p6 m# Y2 X
│ │ 06_The_Inner_Product-_Orthogonality.mp4
. q% e7 f4 K' P6 J; j$ p* A) a; G │ │ 06_The_Inner_Product-_Orthogonality.pdf9 |4 o: R) u5 ^9 V- V
│ │ 06_The_Inner_Product-_Orthogonality.srt8 X* N! Y+ {' b4 Q, }
│ │ 06_The_Inner_Product-_Orthogonality.txt( _; L) S; ?: ]- N) s) d. r+ S4 [/ z/ Y
│ │
$ }3 Q# n- u9 x d6 ]; q3 o │ ├─08_Week_7-_Orthogonalization3 O2 @5 t8 a9 U, x) u/ U: J
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.mp4( h8 l x$ E2 V: {; T" U
│ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.pdf
5 B) v4 k( U# H3 |1 N0 h% G0 W │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.srt
' b& j p8 \* J2 b3 i+ G' N │ │ 01_Orthogonalization-_Finding_the_closest_point_in_a_plane.txt
* t$ e. U/ \) W │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.mp4- ]" _# x0 d# s: S- j1 X2 Y
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.pdf
3 ^ C Y7 N6 x/ J9 S │ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.srt5 x2 |- R* i$ O+ J- K# I
│ │ 02_Orthogonalization-_Projection_orthogonal_to_multiple_vectors.txt
! @8 E2 s4 W4 R/ v( I+ l │ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.mp4
\: e& [9 u( E, h. Q1 A │ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.pdf/ m% C+ f( G" k/ E" p
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.srt$ d3 O: ]0 F5 u1 n! q4 A/ s
│ │ 03_Orthogonalization-_Building_an_orthogonal_set_of_generators.txt
" J' ^4 o! |: N6 z9 X │ │ 04_Orthogonalization-_Computing_a_basis.mp42 D4 I9 Q" Z. m% G/ \; H1 w8 C3 O
│ │ 04_Orthogonalization-_Computing_a_basis.pdf
2 A2 D- ]! _, w │ │ 04_Orthogonalization-_Computing_a_basis.srt; C$ i) `* s2 y' L7 F
│ │ 04_Orthogonalization-_Computing_a_basis.txt
0 i" J$ {: R8 e/ c) m, Z4 D, D │ │ 05_Orthogonalization-_Orthogonal_complement.mp4$ |; k7 q* f+ ]' A1 H
│ │ 05_Orthogonalization-_Orthogonal_complement.pdf' V3 `: j; T' ]% G6 H4 y" o% N! d; f
│ │ 05_Orthogonalization-_Orthogonal_complement.srt
' @/ B# r' g7 D( P │ │ 05_Orthogonalization-_Orthogonal_complement.txt
. s4 o3 t6 U1 b/ n- y │ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.mp4
' c T Y1 y! r0 d' V │ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.pdf3 T3 u4 O* d/ o* S
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.srt# Z# _ [1 h9 @2 r3 k
│ │ 06_Orthogonalization-_Two_ways_to_find_a_basis_for_the_null_space.txt
. q9 D. r, n/ i4 y1 D │ │ 07_Orthogonalization-_The_QR_factorization.mp4
1 r$ L3 [* O1 Q% A) I( o; q( S │ │ 07_Orthogonalization-_The_QR_factorization.pdf6 y3 p) x! \5 L$ r( @# A
│ │ 07_Orthogonalization-_The_QR_factorization.srt
# j1 N( T( j$ {6 I │ │ 07_Orthogonalization-_The_QR_factorization.txt
: a$ W0 W* `) q' E3 A+ \! E. a5 ? │ │ 09_Orthogonalization-_Applications_of_least_squares.mp4
! T: c% B/ s7 |- M6 Z) B! ~& d │ │ 09_Orthogonalization-_Applications_of_least_squares.pdf" M4 r; q u# m: M+ E
│ │ 09_Orthogonalization-_Applications_of_least_squares.srt- W7 o' m3 H0 D3 N$ }8 ]; ^6 f$ h
│ │ 09_Orthogonalization-_Applications_of_least_squares.txt* a# h- ?& m6 G. C
│ │ + j0 H2 k' r+ p& {" C* ?2 M
│ └─09_Tutorials
4 P) i" f* Q, L, p5 E │ 01_How_to_submit_assignments.mp4
- ^" j, m# `3 E& ?9 o- v7 |# `: \ │ 01_How_to_submit_assignments.srt- Q3 [/ S4 {* I% x
│ 01_How_to_submit_assignments.txt J9 a0 g: r# L6 N7 [2 t8 u
│ / p2 \$ r W7 V5 N3 y* J1 P% w
├─Big Data, Cloud Computing, & CDN Emerging Technologies
+ L4 j. \" H3 `% C$ x │ └─Big Data, Cloud Computing, & CDN Emerging Technologies
/ o# e1 s7 _ D* w8 l. W │ │ 下载说明.txt
" W& W' ^3 w* l4 k │ │ 关注我们.png7 l; ^! I: i$ S1 ^) i
│ │ 攻城狮论坛=网络技术+编程视频.url
3 o5 D. m/ a7 b3 _# C& b │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt# g1 R" S5 q1 j3 G. u! a
│ │
1 N; f2 d1 |* j" O) m. U │ ├─01_cloud-computing
( ]' {8 ]- r8 K9 k# \1 G( ~ │ │ └─01_cloud-computing+ R0 i) o' z' S m6 F
│ ├─02_big-data9 [& F7 r+ w9 A% ]1 ]" v
│ │ └─01_big-data
~. T: }% d4 { │ │ 01_big-data-examples.mp4
4 Z. v9 n+ y! B4 d& K: ` │ │ 01_big-data-examples.srt
, y# V4 u- Q6 f4 Y4 a2 v* }; ?/ v │ │ 02_big-datas-4-vs.mp4* y, X' Q7 p' c
│ │ 02_big-datas-4-vs.srt4 [9 G3 U0 e7 u* {; K! Q0 ^
│ │ 03_hadoop.mp4
! V n; B8 f4 u. v6 t │ │ 03_hadoop.srt( o/ `& N5 `" C G0 P' R
│ │ 04_mapreduce-vs-rdbms.mp49 I$ b5 M8 D$ Y% V. c0 C, G
│ │ 04_mapreduce-vs-rdbms.srt
' _6 v! k' q4 ]6 k │ │ 05_mapreduce.mp4
# y9 ~0 g/ M V/ ] │ │ 05_mapreduce.srt' b' G8 d' t$ R" k7 I3 \
│ │ 06_hdfs.mp4
3 X7 E, h' u. n! ^6 X7 d │ │ 06_hdfs.srt$ X. B( Z' D0 H
│ │ $ y- C' ^3 @+ Q7 h
│ └─03_cdn-content-delivery-network
+ d( K) @* e% `) r& [: I ├─Duke Image and video processing From Mars to Hollywood with a stop at the hospital1 o9 W. p+ Y) Y- U- k/ H
├─Interactive Computer Graphics4 x# w& D3 y) b3 o
│ └─Interactive Computer Graphics0 q+ U( O* x/ C- c# ~7 H0 W
│ interactivegraphics-Torrent-Week1_All.zip.torrent
$ E' c: L& d# r; U( k │ interactivegraphics-Torrent-Week2_All.zip.torrent
5 M* {1 U- f. u/ z/ `' c │ interactivegraphics-Torrent-Week3_All.zip.torrent
8 I3 @ P' l0 d( P I; S │ interactivegraphics-Torrent-Week4_All.zip.torrent
* { t6 M9 `+ O1 ]$ n │ interactivegraphics-Torrent-Week5_All.zip.torrent
2 s3 V }9 U; m, F2 ~ m6 f: ~ │ interactivegraphics-Torrent-Week6_All.zip.torrent
2 R+ k& U1 g. G; b. k │ interactivegraphics_Torrent_Week1_All.zip
( ^5 L8 a+ _- e" C │ interactivegraphics_Torrent_Week2_All.zip
# ?2 L: |6 R* N# V1 R/ W │ interactivegraphics_Torrent_Week3_All.zip) O* j: z& o5 X }) d) ]6 u( Z/ w
│ interactivegraphics_Torrent_Week4_All.zip* q# u6 m$ K2 R' _/ V/ ^* V
│ interactivegraphics_Torrent_Week5_All.zip7 r& b8 x# Q7 }, O0 q
│ 下载说明.txt
$ m9 G+ N3 ?# D! H$ O0 l, p/ a │ 关注我们.png: w( Y ^( `+ ?
│ 攻城狮论坛=网络技术+编程视频.url
9 C1 y" M {3 R3 { │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt' n; R7 A) ~" Y0 \5 U' p+ R
│ ( K& m; J, [# V9 @" w. C I
├─Malik - Computer Vision
" ~" E6 _) w4 f5 \ │ └─Malik - Computer Vision0 q4 K( r- i, A+ z7 m
│ 001_Overview.mp47 P- `: ^2 ]* I# t8 F
│ 001_Overview.srt
3 q, P) K2 c4 H% m" B │ 002_Fundamentals of image formation.mp4
3 H% ]% X1 ?* M+ B8 X' K# F6 g( \5 b │ 002_Fundamentals of image formation.srt
5 v& e2 C ~6 Q" M0 N7 X( B' J │ 003_Fundamentals of image formation - part II.mp4! i3 V$ _( K, f E2 D( l/ \& K
│ 003_Fundamentals of image formation - part II.srt L! q$ N% f0 @( ~ x
│ 004_Rigid body motion.mp4: q* ]5 C& o7 @* l
│ 004_Rigid body motion.srt8 o7 B& C% Y7 M5 a+ H l7 Y3 y
│ 005_Orthogonal transformations.mp4; ^3 r) e M" P8 \- W0 S
│ 005_Orthogonal transformations.srt/ t! }/ q8 m3 J, H9 [$ V$ S2 z
│ 006_Orthogonal transformations - Orthogonal Matrices.mp4
# z6 O# i/ N% b* O# C% l │ 006_Orthogonal transformations - Orthogonal Matrices.srt
" ^5 I! v2 j, }& V& W │ 007_Orthogonal matrices - Rotations and reflections.mp4
& F' b8 l/ G( W A │ 007_Orthogonal matrices - Rotations and reflections.srt
3 T6 A; j! S" D │ 008_Parametrizing Rotations in 3D.mp4- A3 n( M; C* J( ^8 v& C/ _
│ 008_Parametrizing Rotations in 3D.srt) ^. p0 [2 G, W* h$ t) y
│ 009_Euclidean, Affine and Projective Transformations.mp4
5 u* \5 ^! @0 Y7 m) ] │ 009_Euclidean, Affine and Projective Transformations.srt
0 T# O+ o6 S* w7 N │ 010_Dynamic Perspective - I [15 mins].mp4! @& L4 ~; J5 U- ?5 I
│ 010_Dynamic Perspective - I [15 mins].srt C" X5 {1 z- L4 x
│ 011_Dynamic Perspective - II [25 mins].mp4% `3 M* b1 y- m3 { r3 [5 |
│ 011_Dynamic Perspective - II [25 mins].srt" h. |# Z5 z3 M* J5 Q
│ 012_Binocular Stereo I [23 mins].mp4
4 i: K4 H: L! O6 B7 L0 G+ | │ 012_Binocular Stereo I [23 mins].srt' ~; [! u, E3 |- q# c
│ 013_Binocular Stereo II [17 mins].mp4
: e5 n6 F) f: [ │ 013_Binocular Stereo II [17 mins].srt
- Y" L- B) _6 f │ 014_Binocular Stereo III [13 mins].mp4$ N0 n1 U7 b( [, C& _! O7 B H- ~
│ 014_Binocular Stereo III [13 mins].srt
8 W1 `0 y U; k5 M9 ]8 r x │ 015_Binocular Stereo IV - The Essential Matrix [26 min].mp4
`* W/ a6 o" Y │ 015_Binocular Stereo IV - The Essential Matrix [26 min].srt
: Q! x& J9 }1 T& E │ 016_Radiometry.mp4; x y/ I! M: A! A5 _
│ 016_Radiometry.srt
& p7 B0 D. E/ e7 z, S/ B; ] │ 017_Image processing.mp4
3 x% {: X: X, z: X# K- P( B │ 017_Image processing.srt
; W# `( c5 K; q │ 018_Image Processing2.mp4: g2 w$ e. u7 w' L8 v
│ 018_Image Processing2.srt
) [* |% o( F/ ~# E' K) f │ 019_Orientation histograms [7 min].mp4& n6 c1 H4 H! W
│ 019_Orientation histograms [7 min].srt& b0 ^( [" O B
│ 020_Handwritten digit recognition - Introduction.mp4
' q# E" c! t3 V6 e9 {% t │ 020_Handwritten digit recognition - Introduction.srt
" a+ z3 m! ?, \! J: q m2 ^ │ 021_Support Vector Machines.mp4
/ V2 l9 V. G7 y6 g │ 021_Support Vector Machines.srt- }9 k1 S0 W2 E% V
│ 022_Transformation Invariance and Histograms.mp48 O. E4 b( m V% L! a; F
│ 022_Transformation Invariance and Histograms.srt
$ X7 F5 l- m# x$ o │ 023_Digit recognition using SVMs.mp4& ]' B8 b2 q8 m3 ]. _
│ 023_Digit recognition using SVMs.srt
9 m( n0 s' S Y8 E0 Q │ 024_Random forests.mp4
|' q! C- v# t │ 024_Random forests.srt& Y6 e# J/ N! {% W9 h0 m
│ 025_Detection of 3D objects.mp4
" V; B/ U& V( z/ \ │ 025_Detection of 3D objects.srt
! v1 k% T6 Z4 u. a │ 026_Concluding Remarks.mp4
- ]7 N/ V4 Q" r │ 026_Concluding Remarks.srt
+ [7 _4 t2 R+ Y) a! ~- ` │ 下载说明.txt
5 v( b0 k6 A% l) M; P │ 关注我们.png3 |/ F+ A, H" q3 I$ I# I
│ 攻城狮论坛=网络技术+编程视频.url+ W- h1 {$ k; F7 \$ z- a$ \: c
│ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt+ |+ r& B2 t! a; {& I4 U
│ , W J+ M" p5 d- e8 s7 k; z; ]) e
├─Practical Machine Learning
. T( a8 E5 ~- w( V- w0 \! b │ └─Practical Machine Learning, |8 l$ u9 b" D1 J
│ │ 下载说明.txt. {' Q& F' P( r1 ?1 n3 D$ V9 ~
│ │ 关注我们.png- W8 p. \' u6 U- j
│ │ 攻城狮论坛=网络技术+编程视频.url
P" Y% L. N8 i S3 i │ │ 解压缩密码是方括号里的内容 [攻城狮论坛 bbs.vlan5.com].txt6 |4 @9 H ]0 D6 `+ T) V$ u& i
│ │ 1 T4 z2 L; t) A' U
│ ├─Week1
5 ]7 M) j7 k( i, v5 K4 j3 x │ │ 001predictionMotivation.pdf
2 y+ o. U) W D │ │ 002whatIsPrediction.pdf, } R: S, @7 [5 S# H' C5 O
│ │ 003relativeImportance.pdf
0 j9 _7 S& n& K0 Y │ │ 004inOutSampleErrors.pdf9 Q( r( Y4 |% ~0 _
│ │ 005predictionStudyDesign.pdf
$ X% @. r) a7 `0 g │ │ 006typesOfErrors.pdf4 l8 `( f" `8 U0 a3 h
│ │ 007receiverOperatingCharacteristic.pdf4 i/ M0 ?: b" [/ n
│ │ 008crossValidation.pdf
/ n' C( r0 G; O$ ^) m( ^' W2 | │ │ 009whatData.pdf+ }, _1 {, j9 v$ N: V; J
│ │ 1 - 1 - Prediction motivation (8-26).mp4
( h+ Y* q. l" ~ l8 M% h4 l │ │ 1 - 1 - Prediction motivation (8-26).srt4 f- u9 l; p3 ?! n, t C0 Q
│ │ 1 - 2 - What is prediction- (8-39).mp43 E+ E0 f% g7 `0 B6 Y
│ │ 1 - 2 - What is prediction- (8-39).srt
4 N. P( p F- f │ │ 1 - 3 - Relative importance of steps (9-45).mp4
3 ]* w q$ i/ i% b$ J& V0 q │ │ 1 - 3 - Relative importance of steps (9-45).srt Z: c, F- D# m1 ^2 p( t# R
│ │ 1 - 4 - In and out of sample errors (6-57).mp4
0 S8 i" v- w8 _" K: R │ │ 1 - 4 - In and out of sample errors (6-57).srt. K+ w# Q. y e) G }
│ │ 1 - 5 - Prediction study design (9-05).mp4' h! C0 `2 }$ l
│ │ 1 - 5 - Prediction study design (9-05).srt' r- h1 U4 L5 o3 X8 b
│ │ 1 - 6 - Getting Data Overview (1-34).mp4
3 U5 V) _3 A8 w* r% e; `5 l+ c& t) n │ │ 1 - 6 - Types of errors (10-35).mp4' m2 M9 X$ i4 N$ P2 K# |
│ │ 1 - 6 - Types of errors (10-35).srt
9 Y% j2 g0 D$ H, P3 \ │ │ 1 - 7 - Receiver Operating Characteristic (5-03).mp40 z' E: z% e: `" H
│ │ 1 - 7 - Receiver Operating Characteristic (5-03).srt* w; G5 n% z [; s% g* | z1 \8 r% [/ o
│ │ 1 - 8 - Cross validation (8-20).mp4( N8 @$ H# u6 V( m3 A+ b
│ │ 1 - 8 - Cross validation (8-20).srt
9 @0 t+ F% r* E# n& p+ G1 d │ │ 1 - 9 - What data should you use- (6-01).mp44 ~" G- D( @' S
│ │ 1 - 9 - What data should you use- (6-01).srt P$ s0 y2 v5 C9 r% Z! m ]
│ │
( ]- e7 S5 C$ `- t │ ├─Week2+ b3 B& I4 `: N6 g
│ │ 010caretPackage.pdf
0 k" j; ]& }( b* M$ t+ [0 E9 X- @ │ │ 011dataSlicing.pdf
1 F0 p7 _+ x0 I. e5 G+ C │ │ 012trainOptions.pdf3 \1 n: ]3 V7 c# U- V: V
│ │ 013plottingPredictors.pdf
( }7 u- Y( B) i' M; ` │ │ 014basicPreprocessing.pdf
2 V7 a4 _7 q7 {' u/ I │ │ 015covariateCreation.pdf
5 w- Y+ V$ V r6 L# H$ `0 G │ │ 016preProcessingPCA.pdf
% K& ^% d9 ~, l0 ` │ │ 017predictingWithRegression.pdf" ?3 {% f6 n; H a* d% J# o
│ │ 018predictingWithRegressionMC.pdf |$ j& o. v' Q* K
│ │ 2 - 1 - Caret package (6-16).mp4
% _) } J' C( m8 [. n& `+ Y! u: Y │ │ 2 - 1 - Caret package (6-16).srt& t3 o; ~" p* ^2 a
│ │ 2 - 1 - Caret package (6-16).txt( \# X" E* |- Z* h
│ │ 2 - 2 - Data slicing (5-40).mp4# N+ r9 j9 N3 l$ {1 j
│ │ 2 - 2 - Data slicing (5-40).srt
* A% D9 ]! _8 v S, S- I │ │ 2 - 2 - Data slicing (5-40).txt4 t8 [: j* e! G* q; l* G* m6 k) n
│ │ 2 - 3 - Training options (7-15).mp4
( Y( g3 l( \/ v& [, R: u │ │ 2 - 3 - Training options (7-15).srt i! L5 u) v! G+ n. q- e3 ^ P
│ │ 2 - 3 - Training options (7-15).txt) b% C% T( C0 M' Z; @
│ │ 2 - 4 - Plotting predictors (10-39).mp40 w; [( d+ B5 e: v' ]& g- T5 @
│ │ 2 - 4 - Plotting predictors (10-39).srt
# i) @, s" U" k4 j- l │ │ 2 - 4 - Plotting predictors (10-39).txt$ Z* a; \( y/ |; F. ?! E" z
│ │ 2 - 5 - Basic preprocessing (10-52).mp4
' O: f. [7 O/ X: E" U: w: |; q4 O │ │ 2 - 5 - Basic preprocessing (10-52).srt7 E3 s" [% }/ D G
│ │ 2 - 5 - Basic preprocessing (10-52).txt: @3 s# x' O4 c1 H" F9 |# i
│ │ 2 - 6 - Covariate creation (17-31).mp4
; E- h$ q/ V/ P" d: S │ │ 2 - 6 - Covariate creation (17-31).srt
! R6 o0 H5 e2 M │ │ 2 - 6 - Covariate creation (17-31).txt
6 T8 l+ j% q% ~8 ^9 s) x% [" P │ │ 2 - 7 - Preprocessing with principal components analysis (14-07).mp4, H/ k( S$ A; z# x. V" ^
│ │ 2 - 7 - Preprocessing with principal components analysis (14-07).srt) i6 R% N6 o. }, X7 L
│ │ 2 - 7 - Preprocessing with principal components analysis (14-07).txt
1 e7 }8 R- {7 \6 J4 Q8 B4 a U │ │ 2 - 8 - Predicting with Regression (12-22).mp4
: k: t" J* p1 ~ │ │ 2 - 8 - Predicting with Regression (12-22).srt
) s( t) e8 V, E6 v │ │ 2 - 8 - Predicting with Regression (12-22).txt
) N$ a+ N4 x9 S" a& S* A7 o5 U │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).mp4
& r, `% ~- ^# g! R. l+ Y& Y' ] │ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).srt: A) y) e: P5 @4 O' G t/ Z* G
│ │ 2 - 9 - Predicting with Regression Multiple Covariates (11-12).txt
( I: G( [4 O+ k8 w │ │ 4 V+ c, Z' y' S7 k
│ ├─Week3
5 N4 {0 d. _$ r) H! X │ │ 019predictingWithTrees.pdf
/ K3 ^3 Q+ d/ C( d l% H X# R │ │ 020bagging.pdf! D4 m# { ]3 J7 J4 d0 v1 ]- Z) ^7 q
│ │ 021randomForests.pdf% c% j: Y4 G! v; B i
│ │ 022boosting.pdf
: M$ R2 U+ y7 d. z | │ │ 023modelBasedPrediction.pdf
- C' O# F$ n% Q# K; e │ │ 3 - 1 - Predicting with trees (12-51).mp4& s% }" X7 \- j, S6 ?
│ │ 3 - 1 - Predicting with trees (12-51).srt# ^+ T1 z; a$ \0 J, C. ~
│ │ 3 - 1 - Predicting with trees (12-51).txt
6 r% y ]( b+ |7 g9 l │ │ 3 - 2 - Bagging (9-13).mp4
. B9 [, B0 T% ]! h J │ │ 3 - 2 - Bagging (9-13).srt
: l6 P3 e7 h, Z' v, B │ │ 3 - 2 - Bagging (9-13).txt2 M3 J" T2 ~# \! R2 s- d* B) q, [
│ │ 3 - 3 - Random Forests (6-49).mp4" g/ p, Y! ?& h# U+ F
│ │ 3 - 3 - Random Forests (6-49).srt" o. E7 {& ], u& A) }- C. a
│ │ 3 - 3 - Random Forests (6-49).txt
1 q2 t2 x& r- F n/ M │ │ 3 - 4 - Boosting (7-08).mp4* ^/ P2 M; j' p# V) O; }
│ │ 3 - 4 - Boosting (7-08).srt
9 T2 G1 [1 ?+ c# e$ G$ u3 W# _ │ │ 3 - 4 - Boosting (7-08).txt
# y2 b) P' V: C9 X8 {/ I) Q2 ? │ │ 3 - 5 - Model Based Prediction (11-39).mp4
1 _ E; J5 _$ z' Z │ │ 3 - 5 - Model Based Prediction (11-39).srt
0 a1 V" j% _! ~6 q │ │ 3 - 5 - Model Based Prediction (11-39).txt
6 a# z7 u' A) s5 E! v- F; q# E │ │
; G! l9 B8 f, q0 V+ F; j" @ │ └─Week4
4 O' \6 v1 C/ m7 C. d │ 024regularizedRegression.pdf
# D; d) ]( f. o+ [/ c4 v0 e: @' p │ 025combiningPredictors.pdf' q |1 L9 v- t
│ 026unsupervisedPrediction.pdf3 h- e& G r C# S
│ 027forecasting.pdf
! R1 G- g6 z( E9 H- r. k │ 4 - 1 - Regularized regression (13-20).mp4
; e0 _4 s( @! D │ 4 - 1 - Regularized regression (13-20).srt# D% U/ ^7 X1 ^" m$ A
│ 4 - 1 - Regularized regression (13-20).txt& [/ {4 M/ |/ O- b8 H4 v! a2 {
│ 4 - 2 - Combining predictors (7-11).mp4+ L4 V; H( d' J4 j5 Z; x8 c! O2 u
│ 4 - 2 - Combining predictors (7-11).srt' m8 h6 d* h, l. @. E) B- R
│ 4 - 2 - Combining predictors (7-11).txt Q. E2 ~0 [3 _4 N
│ 4 - 3 - Forecasting.mp43 v" `* B! S( U5 ^
│ 4 - 3 - Forecasting.srt
9 i0 ~! c3 g: k7 v9 [: a( c │ 4 - 3 - Forecasting.txt
5 U6 i9 g3 S2 x │ 4 - 4 - Unsupervised Prediction (4-24).mp44 |/ ^" Y' b4 k" j* |$ r- X
│ 4 - 4 - Unsupervised Prediction (4-24).srt
1 u; f) o' q' R; Z$ S8 c+ { │ 4 - 4 - Unsupervised Prediction (4-24).txt
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├─Sapiro - Image and video processing: ~. `/ H3 R% w7 v
│ 01.pdf/ k) D1 s: v4 x3 |) y3 p( F
│ 02.pdf$ ~4 ?9 z: ~# W( w \
│ 03.pdf
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7 h' f9 h G& M' E J& W; V, D │ 05.pdf
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│ 07.pdf" w/ X* {* [5 a( v
│ 08.pdf
3 _6 o' ]) b+ k/ f │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.mp4
[4 d1 @5 V/ w/ C3 W$ m1 _ │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.srt
: h) N, m! c* x) q" [5 F │ 10 - 1 - 1 - Introduction to Medical Imaging - Duration 0703.txt
/ `+ a1 l" Y1 j │ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.mp4. z! |. b; A3 x* _* f2 e' g; N
│ 10 - 2 - 2 - Image Processing and HIV (Part I) - Duration 2351 - Optional breaks at 1237 and 1839.srt
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│ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.mp4
5 P% @* I6 T# {' n# i4 r │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.srt
' a- G! G5 e% Y# Q) i# `8 }; V │ 10 - 3 - 2 - Image Processing and HIV (Part II) - Duration 1630.txt
1 X; {' k+ Q4 Q& {9 G9 l* h( v │ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.mp4$ T+ j5 c$ h4 }+ e# X+ N s
│ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.srt
9 }$ n; g4 x; n! S, X │ 10 - 4 - 3 - Brain Imaging Diffusion Imaging Deep Brain Stimulation - Duration 2628.txt
0 k7 l$ h% y* E* M │ 10 - 5 - 4 - Thanks.mp4
5 M+ ^0 \ T' [% _ d3 m │ 10 - 5 - 4 - Thanks.srt; t& E8 l! R0 U
│ 10 - 5 - 4 - Thanks.txt
( f, o. T% \( ?" @- Z, f │ 2 - 1 - 0 - Welcome and Start Here.mp4* i4 |) x E' Y7 X) F8 B
│ 2 - 1 - 0 - Welcome and Start Here.srt- E: U. E6 t$ u: M
│ 2 - 1 - 0 - Welcome and Start Here.txt: A/ l: F! Z! S5 k
│ 2 - 2 - 1 - What is image and video processing (part 1) - Duration 1049.mp4
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│ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.srt
' m0 D T; I% K │ 2 - 3 - 1 - What is image and video processing (part 2) - Duration 1040.txt) G& _, _4 P6 k+ b# |, r4 C
│ 2 - 4 - 2 - Course logistics - Duration 0242.mp4% D5 A6 [- `# b2 Z( J$ F1 G( [
│ 2 - 4 - 2 - Course logistics - Duration 0242.srt
0 V0 D7 J. p- Y: q8 R( @ │ 2 - 4 - 2 - Course logistics - Duration 0242.txt0 Q" M% h+ p; |3 d- V
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.mp4 }% m( ]7 D: g2 e$ Y" E" W( ~/ l, m
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.srt. R4 ?+ T8 i, @" L3 U
│ 2 - 5 - 3 - Images are everywhere - Duration 0631.txt! `" ]$ n1 ^. t! @
│ 2 - 6 - 4 - Human visual system - Duration 1710.mp4
( _! i; J5 g( r' Q8 m2 `# k" r) w$ s7 W │ 2 - 6 - 4 - Human visual system - Duration 1710.srt
" ]- x0 \& s0 P& L6 L0 K' H │ 2 - 6 - 4 - Human visual system - Duration 1710.txt
% \0 A0 O4 n: [9 P, F6 d+ E │ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.mp4' e b( I) B& z( [& ~# Q- p5 z
│ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.srt
* g! u; j& Q/ n3 F │ 2 - 7 - 5 - Image formation - Sampling Quantization - Duration 2817.txt s. k$ f: W2 S" q
│ 2 - 8 - 6 - Simple image operations - Duration 1705.mp4$ |* G& x# k2 ]# ?3 y6 T" F
│ 2 - 8 - 6 - Simple image operations - Duration 1705.srt r, M2 Q n5 a2 Z3 N y
│ 2 - 8 - 6 - Simple image operations - Duration 1705.txt+ B6 O* T+ Z5 `7 s- W
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.mp4: M3 R( i; i9 d- Y
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.srt! F7 T% }, n3 v% V! e2 A( m
│ 3 - 1 - 1 - The why and how of compression - Duration 1416.txt/ S( E5 n* `2 \) W
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.mp4
; a" C5 i4 q9 D+ x* S- l │ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.srt x/ e6 W& \6 L3 x- V
│ 3 - 2 - 2 - Huffman coding - Duration 2011 - Optional break at 0653.txt
; [' M& V' T* j+ F& M$ U/ v! `, T │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.mp4
% J0 j- D" y8 {+ r) m# \( k8 k │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.srt
. w& w' Y, I, o W# x1 E │ 3 - 3 - 3 - JPEGs 8x8 blocks - Duration 0537.txt/ q& {, O! E7 \' M$ Q+ L
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.mp4/ X6 G2 K0 V6 L# V2 W; \5 }
│ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.srt
( h+ R1 V0 b3 B5 M) h8 j │ 3 - 4 - 4 - The Discrete Cosine Transform (DCT) - Duration 2532 - Optional break at 1213.txt. l. t: A: T: s5 K! E
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.mp4) {( ^. O) W* y) Z& D# l) E f
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.srt- t6 e \" g O2 c; K
│ 3 - 5 - 5 - Quantization - Duration 2402 - Optional breaks at 0848 and 1718.txt" r6 z( N5 V6 M1 C. X* y% v
│ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.mp4
! x5 \- S& D2 J) g, G0 R9 ?/ g │ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.srt1 M3 T. {& Z8 P6 F& t' s4 e% o, s* y
│ 3 - 6 - 6 - JPEG_LS and MPEG - Duration 1932 - Optional break at 1345.txt
& M$ x' U9 f$ Y │ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.mp4
. r% L% F% h( T1 h) i │ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.srt
5 Q; \0 k/ x# ]: {4 H& \5 b/ w6 J │ 3 - 7 - 7 - Bonus Run-length compression - Duration 0429.txt
# f/ {8 n3 F/ c$ i% q │ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.mp4
) ^+ @) D+ m! D: z! x" v2 U │ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.srt2 Q7 ^7 t" A( e/ X
│ 4 - 1 - 1 - Introduction to image enhancement - Duration 1911 - Optional break at 0833.txt+ n! p* }$ @4 J% p
│ 4 - 10 - 10 - Demo - Median filter - Duration 0131.mp4
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│ 4 - 10 - 10 - Demo - Median filter - Duration 0131.txt: f; G3 \: ]- a3 B) W/ g
│ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.mp4. b! w) J/ e" A$ Y' r* h
│ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.srt
! R- `9 W' [7 F+ h T% c │ 4 - 11 - 11 - Derivatives Laplacian Unsharp masking - Duration 1424 - Optional breaks at 0521 and 1133.txt
! Z9 j2 z% U* I* g7 l │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.mp4
& y8 m" b; `5 \' ~6 u0 n, M, t │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.srt
7 H2 Q; X- u6 M( K │ 4 - 12 - 12 - Demo - Unsharp masking - Duration 0310.txt
8 Q& K) F6 H$ G- T1 B7 | │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.mp45 f( m: b7 C4 y/ f9 E8 ~ G
│ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.srt
2 B: f9 j- h+ o9 q; V$ u( T │ 4 - 13 - 13 - Gradients of scalar and vector images - Duration 0557.txt8 J( U& L6 H, W7 p" K1 |+ Z9 c* W
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.mp4
+ h" d. F) j, A" ~ │ 4 - 14 - 14 - Concluding remarks - Duration 0112.srt, V0 y0 }" V5 {8 E- c5 T+ o
│ 4 - 14 - 14 - Concluding remarks - Duration 0112.txt6 H/ O" U1 H: S! S) Z9 A# p
│ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.mp4
2 x+ F3 H1 v$ f7 P/ Y# M1 Q$ A3 P) w │ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.srt
+ Y4 ] Q5 m9 C5 i0 j │ 4 - 2 - 2 - Demo - Enhancement Histogram modification - Duration 0353.txt
3 x* h/ P# E) x │ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.mp4
. h: d! g8 `& G3 e │ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.srt" q' Q! H/ f0 q' H) Q, ~
│ 4 - 3 - 3 - Histogram equalization - Duration 1956 - Optional breaks at 0440 and 1130.txt3 L3 T% q$ y9 g2 s* x! f
│ 4 - 4 - 4 - Histogram matching - Duration 0831.mp49 @" E( _, t+ `& q! q
│ 4 - 4 - 4 - Histogram matching - Duration 0831.srt
8 W7 O6 [% P9 z1 k( _5 u. h │ 4 - 4 - 4 - Histogram matching - Duration 0831.txt
8 s* x3 b" O- Q Y9 \7 M( R+ H │ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.mp4# f, ], m6 K' y7 Y. Y
│ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.srt! E2 C5 z. \, m; t% D
│ 4 - 5 - 5 - Introduction to local neighborhood operations - Duration 0646.txt$ R$ n; g; n* C
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.mp4' c4 m7 P; [( D; e! o3 g) L
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.srt _$ \. e+ s) Y0 }
│ 4 - 6 - 6 - Mathematical properties of averaging - Duration 1100.txt
+ g3 c2 s+ V( m0 y; Q: | │ 4 - 7 - 7 - Non-Local means - Duration 0727.mp4
6 v$ K/ k, Z/ [ n+ ?% Q │ 4 - 7 - 7 - Non-Local means - Duration 0727.srt& `0 }3 A" o7 T5 s) z1 a
│ 4 - 7 - 7 - Non-Local means - Duration 0727.txt
6 u9 ` q- z1 s2 ?+ ^ │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.mp4
; r) |* w! v. P- T │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.srt
( r) t8 d+ s0 y# Y │ 4 - 8 - 8 - IPOL Demo - Non-Local means - Duration 0338.txt
5 c9 S0 L6 ~( `9 j! Z% N, g │ 4 - 9 - 9 - Median filter - Duration 0720.mp4
7 g1 R7 r0 d4 _. Y$ ^0 D+ Q( \ │ 4 - 9 - 9 - Median filter - Duration 0720.srt( c" ^: ]3 j+ b1 P3 o7 A0 o
│ 4 - 9 - 9 - Median filter - Duration 0720.txt1 K3 m: E: Z" M" j. B/ h
│ 5 - 1 - 1 - What is image restoration - Duration 0749.mp4
/ C+ L: ~: a1 b: x │ 5 - 1 - 1 - What is image restoration - Duration 0749.srt
2 M9 `0 A" ~0 s% ?, X │ 5 - 1 - 1 - What is image restoration - Duration 0749.txt* a0 u+ |! X" ^# b, y% j+ g
│ 5 - 2 - 2 - Noise types - Duration 1243.mp4
* {# S6 S8 B$ K+ Q5 |, ^ ? │ 5 - 2 - 2 - Noise types - Duration 1243.srt
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│ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.mp4) w. B3 I G; Z0 j/ n
│ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.srt: Y5 G. A7 Z. h6 t. p( F5 g: @# C
│ 5 - 3 - 3 - Demo - Types of noise - Duration 0303.txt4 E5 `/ [; }6 L# u
│ 5 - 4 - 4 - Noise and histograms - Duration 0452.mp4
u: _$ q4 m% @ │ 5 - 4 - 4 - Noise and histograms - Duration 0452.srt
! W6 t$ e4 w! G% c% g9 E$ R │ 5 - 4 - 4 - Noise and histograms - Duration 0452.txt. a& |; V8 K6 J8 u) c" F/ ]
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.mp4. ?% H! T" U% r
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.srt' e( u; E8 v, S. L; H" i
│ 5 - 5 - 5 - Estimating noise - Duration 1041 - Optional break at 0503.txt
+ ^+ A9 U- k+ \6 X │ 5 - 6 - 6 - Degradation Function - Duration 1140.mp47 u/ d* @' L5 P) e3 |% z$ y2 u8 u: c
│ 5 - 6 - 6 - Degradation Function - Duration 1140.srt6 r, L4 ^- j7 B7 _2 i
│ 5 - 6 - 6 - Degradation Function - Duration 1140.txt/ t) q. z2 }2 g' t; G
│ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.mp4
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│ 5 - 7 - 7 - Wiener filtering - Duration 1234 - Optional break at 0654.txt, m$ U& F8 ]7 a: [
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.mp4, p5 r) x# X& c4 m k$ W
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.srt# p# C. T& |& |1 W$ \* Y6 I
│ 5 - 8 - 8 - Demo - Wiener and Box filters - Duration 0319.txt* t* D! m5 R6 s& U! a4 M
│ 5 - 9 - 9 - Concluding remarks - Duration 0033.mp48 c7 Q6 H" u4 u
│ 5 - 9 - 9 - Concluding remarks - Duration 0033.srt3 J( S- F T" e5 [0 L( Z! V
│ 5 - 9 - 9 - Concluding remarks - Duration 0033.txt& u& c; z- \+ @" P! L8 c b
│ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.mp4. H5 D, h: h' ]; s% N2 G7 q
│ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.srt
8 F- c6 k9 }5 C0 }, E( n │ 6 - 1 - 1 - Introduction to Segmentation - Duration 0417.txt% W- \+ n( d! c( n- q) b
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.mp42 m [9 u. V) U# z1 ?
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.srt0 `9 J' V7 b6 E# e8 V% W
│ 6 - 10 - 10 - Active Contours - Introduction with ipol.im and Photoshop Demos - Duration 0558.txt
" p7 w! `6 n) u H( i │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.mp4
' ^5 E1 r3 I( r! ]6 P; e6 f │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.srt
5 H! T" j: a# a! A+ ^ │ 6 - 11 - 11 - Behind the Scenes of Adobes Roto Brush - Duration 3129 - Optional breaks at 2030 and 2726.txt
4 o% Z+ _* L5 g: ^! u# E2 |2 [ │ 6 - 12 - 12 - End of the Week - Duration 0021.mp4 u Z v3 i; F
│ 6 - 12 - 12 - End of the Week - Duration 0021.srt
# K" P& [3 w! Y* ]: Q* y │ 6 - 12 - 12 - End of the Week - Duration 0021.txt
' I, w) \2 w& e M" k │ 6 - 2 - 2 - On Edges and Regions - Duration 0517.mp4' W; c y; k% J7 P1 K
│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.srt8 \! \2 k9 A1 t
│ 6 - 2 - 2 - On Edges and Regions - Duration 0517.txt
/ P5 R& \+ K3 `/ R- ?! {$ h │ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.mp4
- } }. ?8 u0 n+ O) d& V. c2 A │ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.srt
8 Z% Q; b% E9 L9 i9 x3 \ │ 6 - 3 - 3 - Hough Transform with Matlab Demo - Duration 2059.txt
1 m( F$ _6 M' p% [4 z5 h │ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.mp4; O2 _+ ]9 x6 [6 r6 w# o" y
│ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.srt
8 b2 K: d, e% }" N6 v │ 6 - 4 - 4 - Line Segment Detector with Demo - Duration 0320.txt
; f8 J# v- l; W' L/ M │ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.mp45 M; [1 i' \; \8 E9 ~) H
│ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.srt; C4 O$ J8 S' I
│ 6 - 5 - 5 - Otsus Segmentation with Demo - Duration 1425.txt
|. f- n0 J4 s0 i* p │ 6 - 6 - 6 - Congratulations - Duration 0017.mp4
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0 c5 S e- m* l; {/ l B- s& r │ 6 - 6 - 6 - Congratulations - Duration 0017.txt) S: |! s# \% [! ^) O+ i/ y1 m
│ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.mp4" y, ?) G+ c0 U Y* k& D6 w7 I
│ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.srt
' C: U) V8 [. { │ 6 - 7 - 7 - Interactive Image Segmentation - Duration 2113.txt- Z: Z0 ~6 d: i! d8 f9 t
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.mp4+ w w. c) n4 K# [( ?" p3 x% {
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.srt! s+ y- P' ] [: U$ B
│ 6 - 8 - 8 - Graph Cuts and Ms Office - Duration 0934.txt
" y" T1 L7 F5 T# E2 f6 a: B │ 6 - 9 - 9 - Mumford-Shah - Duration 0550.mp4& V" Y" N* W$ z0 x. G
│ 6 - 9 - 9 - Mumford-Shah - Duration 0550.srt
( m3 D9 S* E. O% B │ 6 - 9 - 9 - Mumford-Shah - Duration 0550.txt
5 x) \8 T! ]# h5 c$ x' @ │ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.mp4
9 t' z% C$ t. ] |# B& Z$ d- C1 F │ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.srt
: _' X0 b% R- L1 T │ 7 - 1 - 1 - Introduction to PDEs in Image and Video Processing - Duration 1022.txt
o8 y2 ]+ L! p! m$ ]0 b( p8 t# x4 C │ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.mp4# y. }: A. @4 Z# [( I9 M
│ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.srt0 c# L/ q2 u( \1 v
│ 7 - 2 - 2 - Planar Differential Geometry - Duration 3833 - Optional breaks at 1246 2103 and 2941.txt
4 C) g: A: U p- X& J( X8 U) H% J) b │ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.mp4
( c) t# p" I' q2 m% ~: ] │ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.srt
. H! x& _+ l f; r3 d- E2 F │ 7 - 3 - 3 - Surface Differential Geometry - Duration 1143.txt
% ?+ @; r* a6 D7 N0 q │ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.mp4
, J3 }" Y5 s' l% `3 W( d) g+ v │ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.srt! A9 }) P" \! u: ]5 p) f# Y
│ 7 - 4 - 4 - Curve Evolution - Duration 3110 - Optional breaks at 0850 1925 and 2422.txt: k3 D2 x) x0 `; U
│ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.mp4
/ @7 m9 D0 w9 e) K; d │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.srt
- k: Y! N5 C" q' J) c │ 7 - 5 - 5 - Level Sets and Curve Evolution - Duration 2534 - Optional break at 1430.txt0 k7 V8 p5 z. ?
│ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.mp4- g. F) t3 Y; k6 j( A/ u
│ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.srt4 @/ K$ O; e( I2 f0 O4 S+ d9 D
│ 7 - 6 - 6 - Calculus of Variations - Duration 1403 - Optional break at 0623.txt6 K( M, q7 K1 |# v% z6 j3 s
│ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.mp4+ m+ C; {1 O G! |7 `2 i& a
│ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.srt
+ d" h+ |% i4 |% I │ 7 - 7 - 7 - Anisotropic Diffusion - Duration 1117.txt
( L$ @" }& i8 z* K* n9 O) a; \ │ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.mp4
. Q* R- ]$ w: i( P │ 7 - 8 - 8 - Active Contours - Duration 1657 - Optional break at 0623.srt
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│ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.mp48 ^+ g3 W: h* C" D# K. @0 K4 f
│ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.srt- g8 q ^1 R9 F
│ 7 - 9 - 9 - Bonus Cool Contrast Enhancement via PDEs - Duration 0832.txt
$ V' I4 i. E0 u/ V) C1 Z │ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.mp4
) F3 T5 G- L& f C: X/ X │ 8 - 1 - 1 - Introduction to Image Inpainting - Duration 0816.srt
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│ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.mp4
5 V8 J3 e* ?! ~3 g4 H │ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.srt
% k2 b9 |( o- t │ 8 - 2 - 2 - Inpainting in Nature - Duration 0501.txt
2 R* O- _) h; i. |% ]" @3 q │ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.mp4( ]9 J7 V2 C' @
│ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.srt
+ \* s- l" m8 D' r, j9 j │ 8 - 3 - 3 - PDEs and Inpainting - Duration 2159 - Optional break at 1331.txt$ p' E9 _- n/ A9 w* y) O6 U
│ 8 - 4 - 4 - Inpainting via Calculus of Variations - Duration 1532 - Optional break at 1006.mp4
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│ 8 - 5 - 5 - Smart Cut and Paste - Duration 0751.mp4. z* [/ w# J' c; i
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