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斯坦福大学吴恩达机器学习视频教程 带中英文字幕学习笔记

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    发表于 2017-10-25 01:16:59 | 显示全部楼层 |阅读模式
    1 - 1 - Welcome (7 min).mkv
    3 A* C2 P7 ?. {: P& ]* y+ `) z1 - 2 - What is Machine Learning_ (7 min).mkv9 o2 N5 \( }% k
    1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    ! ?( w1 N6 o/ s- Y7 D4 y+ I1 - 4 - Unsupervised Learning (14 min).mkv2 |6 y5 V2 N# O; K$ l5 [
    2 - 1 - Model Representation (8 min).mkv( A5 P9 z( Q! O: @7 f
    2 - 2 - Cost Function (8 min).mkv
    . s1 N7 e& M; K2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    " R) \  D+ s: j( n: r: N6 |2 x2 - 4 - Cost Function - Intuition II (9 min).mkv% R  s2 N& j/ y- q) f9 E
    2 - 5 - Gradient Descent (11 min).mkv
    3 e) R. m5 o0 ^) U& E0 q8 G$ f2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    " h- o. Z  H5 z3 W3 v2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    # q+ x3 k& {7 |; D0 R3 o6 O5 e2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
    2 L" ~/ e4 Y7 N, j3 - 1 - Matrices and Vectors (9 min).mkv
    * F* m9 A5 D3 Q3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    ; i: `+ V2 [! r6 {3 - 3 - Matrix Vector Multiplication (14 min).mkv
    " u9 B5 \; m' A7 G5 m1 ^; y  q3 - 4 - Matrix Matrix Multiplication (11 min).mkv
    $ |$ {4 J+ A  i  J3 - 5 - Matrix Multiplication Properties (9 min).mkv) t/ [4 c5 G2 `% S* K- M) o" C7 n$ l
    3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    ) w% |( j5 s; Q, y* k4 - 1 - Multiple Features (8 min).mkv
    ( C6 U. Y4 n) {' E5 k2 a+ {. [4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
      z  ~9 ~! O( j5 G4 m4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv+ Y3 D; a$ U7 ~& }; D1 \
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
    ! P* P$ P7 n4 q- j* K" T' f4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m
    / I1 p' r* y  A1 y! q& Z# O9 {# i4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    / D+ Q0 g  q) n) T& p' R# G4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    1 j3 i7 G7 g( @9 u0 \5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F
    ! O* C: D2 L& y( R5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N- u" |, b7 m$ y, R/ D+ `3 K
    5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z# O* b3 T3 ^- M# F
    5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    5 l) s. j3 h, a$ J3 I6 ~5 v5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u) z3 a8 {& \3 S4 z, \  `/ `
    5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \/ C7 s" S+ N6 |$ U% d. g* E; r5 b8 @
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y
    , Q8 s  J, b4 P, k0 m4 E6 - 1 - Classification (8 min).mkv
    ; F* p, q; z4 ~+ W$ v# c" \1 [6 - 2 - Hypothesis Representation (7 min).mkv0 C" V4 S3 a/ Q1 j+ r
    6 - 3 - Decision Boundary (15 min).mkv* l1 F/ G  i6 |
    6 - 4 - Cost Function (11 min).mkv
      F/ h% D% i- e& ~3 |& F6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y( n4 @0 u* T3 d5 H% Z
    6 - 6 - Advanced Optimization (14 min).mkv
    3 r- G' ]9 }, _7 C4 \* }5 v# R) C3 M6 Z6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z
    3 e9 S' D( y1 g* V; w9 `7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    + ?+ o! B8 t: T1 `: R7 - 2 - Cost Function (10 min).mkv9 ~* E! _% e1 P% l5 o
    7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    * {2 w! [; B+ U' a0 c& e7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A6 |1 M# ~' D4 L8 E. a, d. D' M
    8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e
    & L/ j( P2 B, D( `0 Z8 - 2 - Neurons and the Brain (8 min).mkv" w; S2 h  E! G% ^
    8 - 3 - Model Representation I (12 min).mkv0 K) H2 M4 c. ~) a& u
    8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
    & }" V6 S! `6 x( |, ]8 - 5 - Examples and Intuitions I (7 min).mkv) C4 T3 }0 Z/ \& B; y1 d
    8 - 6 - Examples and Intuitions II (10 min).mkv
    1 O% o/ v1 K6 V8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z
    ( h# N9 o( P$ ]$ n0 }9 - 1 - Cost Function (7 min).mkv
    - I6 {. }; t- L& n9 - 2 - Backpropagation Algorithm (12 min).mkv$ X3 k% e/ D8 y; T1 q, x, e/ @- t
    9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @* Q/ [. d5 M# n% x
    9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L
    " E9 G! T, O9 x3 R* S9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U
    & j+ ^4 Y. [  [+ o) ]3 u" `9 - 6 - Random Initialization (7 min).mkv
    6 J" T" w% B; }) w" i5 s9 - 7 - Putting It Together (14 min).mkv
    & H7 \; F5 E; r/ n- D# F4 B5 L9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E
    , R; L6 o2 H" K. p# `: [10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e9 ?1 j( \% {) f2 S2 J* v: P
    10 - 2 - Evaluating a Hypothesis (8 min).mkv
    . e, o4 H. Q$ ~10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
    - r$ J) o3 l# J3 D4 F10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G, Y& F5 A! f( x9 P4 |' D
    10 - 5 - Regularization and Bias_Variance (11 min).mkv% b+ b# x# ?/ }7 e" ~! c2 N
    10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M4 q; m2 L* l3 O. K- w; W: E
    10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
    " I7 `* i6 P1 J' P# P11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    5 A8 p! s7 x  n+ M8 B7 U# }1 ~11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v, B, X: _) ~$ O* P& }
    11 - 3 - Error Metrics for Skewed Classes (12 min).mkv+ ?: K+ m5 \. _  ]- g6 O+ J
    11 - 4 - Trading Off Precision and Recall (14 min).mkv% j. w" t) y: h) _* V
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a
    ( B8 b  c7 l( r7 p) j) G7 }- S12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
    + _0 I  y; k/ }  p3 O6 B: i5 P12 - 2 - Large Margin Intuition (11 min).mkv- ?" }4 l: a2 r. Z9 {) L3 m/ _
    12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv( t: z6 H3 M1 {7 K4 n: F
    12 - 4 - Kernels I (16 min).mkv
    1 o/ j/ ^8 f, K- y" w12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M7 U+ F0 a2 u( _5 I" T$ G  K7 m
    12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j
    + [7 i' p& O" Y! i, W/ V, ~# ^13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    ( X3 e  ~; k& R5 A: U$ F13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k) \9 k) C6 J$ b) {0 z: a
    13 - 3 - Optimization Objective (7 min)(1).mkv
    - F2 b1 I, L  E4 {) v0 Z. K9 i13 - 3 - Optimization Objective (7 min).mkv
    ) N1 U% B4 f7 i5 U8 F, Y3 d, F6 A4 h13 - 4 - Random Initialization (8 min).mkv
    2 a) w" S; w, \% c& U3 u6 |13 - 5 - Choosing the Number of Clusters (8 min).mkv. K/ {1 u/ h' ]7 ^+ X
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q
    5 H+ H, z  w9 P  Y7 W. K14 - 2 - Motivation II_ Visualization (6 min).mkv3 t1 H2 x+ b) a1 O
    14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
    ! \' ]$ s$ _5 w9 F# t14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [( i, l" d& w2 C- }3 Q
    14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g2 z# N2 _8 o' F3 c$ Q+ K2 f) g
    14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @2 x* w' G3 G% Q4 k% o8 @" L/ y3 \
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W( u' A3 y. d+ ^; [2 |) Q/ ~# w* b1 ~
    15 - 1 - Problem Motivation (8 min).mkv* C5 J( R# Z0 T+ x" B- g
    15 - 2 - Gaussian Distribution (10 min).mkv& j; ]3 x$ C8 @- x4 J
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A2 y2 L4 A1 m& o5 f: r, B2 f& S
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    " W* U& ?2 T$ F9 k# \2 Y. L15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
    * h% o/ u) Q) S) y: L$ j15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q0 c, x' T' F* U: |$ i$ z, P
    15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U
    . z# S0 ^+ }: G# J7 F4 n- ^# }15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e9 R# ?! Y" y$ N
    16 - 1 - Problem Formulation (8 min).mkv% Y5 j0 \9 c0 Z" f1 [
    16 - 2 - Content Based Recommendations (15 min).mkv
    / N- h2 b, w  M, _8 ?. S16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e
    " ~- [* \0 _! N' v8 d( n16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
    1 W/ v2 l% |% H# _# z2 G16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
    6 i3 W' c; s9 g! ^; v1 `6 o# j16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv2 b4 a/ B. P, W+ L+ S6 i
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R, Q( U- ]' `# G" n* R  t! i
    17 - 2 - Stochastic Gradient Descent (13 min).mkv
      b! \. Y! m! b  m+ Y! F% g2 L17 - 3 - Mini-Batch Gradient Descent (6 min).mkv: d0 f4 J# A  f% D0 J" W. C/ Q
    17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
    3 d' y+ F# s& v5 @& Y( b& u3 M17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    & ]- Y( M' C/ V' W7 Y17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
    ; ?6 Y3 L; C7 w- v) [, k18 - 1 - Problem Description and Pipeline (7 min).mkv
    ! [' R8 q" X  j$ k) q7 A8 z# t18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    2 Q! x+ A9 e$ E4 J18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L
    2 L8 N/ V9 E. }18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    " Q1 _' ^% Z2 W19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v: F- n: \- D  X* K
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    发表于 2017-10-25 16:50:33 | 显示全部楼层
    斯坦福大学吴恩达机器学习视频教程 带中英文字幕学习笔记
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    发表于 2017-10-25 06:58:28 | 显示全部楼层
    O(∩_∩)O谢谢
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    发表于 2017-10-25 11:53:07 | 显示全部楼层
    thanks!!!
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    感谢分享
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