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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 |" _$ t8 @  c  E1 - 2 - What is Machine Learning_ (7 min).mkv1 r3 x* h+ v$ t; n* M
    1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E8 S( I8 X2 [' }0 m8 n2 R$ L! p
    1 - 4 - Unsupervised Learning (14 min).mkv
    7 K! t  D1 ]( ]# Y2 - 1 - Model Representation (8 min).mkv
    3 _$ \0 O) G' I: p! v$ L2 - 2 - Cost Function (8 min).mkv
      P4 v0 R( I/ |2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |5 B/ y& |3 j) G- s
    2 - 4 - Cost Function - Intuition II (9 min).mkv/ \3 q. y( S- e+ J- [+ O. R: e
    2 - 5 - Gradient Descent (11 min).mkv
    8 M, l4 [; C! x0 [2 Q0 L9 `2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    3 O: v7 B9 i6 k* g+ v/ R: ]2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    0 @9 K5 u, n4 s" }5 e- D1 Q2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b; ?9 ~6 K: T! @* J4 C
    3 - 1 - Matrices and Vectors (9 min).mkv
    0 M! U% x1 U3 v1 I  D8 j1 K* n4 o3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?7 F: i( j2 K2 Y4 D$ J
    3 - 3 - Matrix Vector Multiplication (14 min).mkv
    + f$ a2 q6 r5 F+ z3 - 4 - Matrix Matrix Multiplication (11 min).mkv1 L; n2 W  w% h+ ~" U
    3 - 5 - Matrix Multiplication Properties (9 min).mkv
      W0 u' R9 a( w3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f( w4 i* ?$ ]0 o& w' W! ~
    4 - 1 - Multiple Features (8 min).mkv
    8 u  R6 M. j0 _% ]4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv1 C9 `7 ]9 `$ u) n, `  [
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv" M: c1 V/ ?4 }; J
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
    2 f5 `) }, e' T' i4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m# H9 J, b/ |+ g8 W
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X4 T) \' i- s' l% Y8 N8 M
    4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    6 k; O- q$ w. a/ V5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F& o$ ^% ]+ m( ^% z; Z/ f2 O: Z
    5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N
      @, N6 H1 m9 F4 |3 i5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z
    7 N% |* E) n' ~) z5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    4 T. {3 O- D1 {' f5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u5 X- U6 B" |1 {  J. i- `+ H1 r9 z! h
    5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \
    : C8 \: X, c0 T  |# \* e/ R+ q% Q5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y7 l# Y4 l2 t! I) `, }
    6 - 1 - Classification (8 min).mkv" O/ [2 R4 t, j& z! n6 }
    6 - 2 - Hypothesis Representation (7 min).mkv. z/ D, |( M  C$ a7 V& F* W
    6 - 3 - Decision Boundary (15 min).mkv
    * k- a; m' Z2 n5 }" g6 - 4 - Cost Function (11 min).mkv
    : C& \6 g( W/ W/ M3 O6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y
    * E. G8 _1 L2 [6 - 6 - Advanced Optimization (14 min).mkv( m, ?/ L' E& p, w
    6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z. m6 y; N( e; o7 f+ @3 k, ]$ i
    7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z" N- a2 L3 w8 X; N1 u' n
    7 - 2 - Cost Function (10 min).mkv
      t5 P" w4 \9 S; X$ Y7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w; ]9 D) I4 M/ H" t
    7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A$ s. I8 v0 ?# e6 S: g
    8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e& E/ H) V( O/ X3 B$ {& n
    8 - 2 - Neurons and the Brain (8 min).mkv
    8 g6 `% h" A; W7 Y8 - 3 - Model Representation I (12 min).mkv
    5 {) m, b' J/ y1 H( }8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H; K! |* ]8 _! R% l% T% v
    8 - 5 - Examples and Intuitions I (7 min).mkv
    7 N9 n& ]6 I* R, u8 ?- i9 M- y8 - 6 - Examples and Intuitions II (10 min).mkv6 _' d6 {1 U& [2 @" _% h7 A
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z
      I; P- Q1 e  ]" f' i+ M9 - 1 - Cost Function (7 min).mkv/ s! j. B: ]5 C1 N0 ^
    9 - 2 - Backpropagation Algorithm (12 min).mkv7 R* K1 z$ o& \" h" U7 e
    9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @
    + e. `7 i& Q9 a5 v$ c- x9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L
    ; T% s2 n! [  Y% O1 D0 o9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U( z- U9 e3 b7 o0 P5 ?: A
    9 - 6 - Random Initialization (7 min).mkv
    3 C  `+ e! d5 `; F& J  e# h9 - 7 - Putting It Together (14 min).mkv
    0 W8 y) I7 \# j3 d" d  u2 q9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E, V' \& b' e1 l8 f! ^+ y* E1 D; |) i
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e( ^' E2 _/ _6 f
    10 - 2 - Evaluating a Hypothesis (8 min).mkv$ y% c, v1 |7 s* @' Y
    10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv, j6 \5 a0 T# A: b3 w6 d
    10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
    ; `; ~; P2 \) o7 f& \8 Q% m10 - 5 - Regularization and Bias_Variance (11 min).mkv
    4 P- }7 P0 Z* f% \1 j% W8 t10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M
    - ~( q/ Y! y5 p; e! [* v10 - 7 - Deciding What to Do Next Revisited (7 min).mkv( H! i) j$ p3 X5 Z6 V: V' v" q7 k
    11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m2 c- |5 _4 M- M; l6 f+ ]/ Q
    11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    , ~" Z" h: v1 `" X: T) Z0 I' P5 `7 \11 - 3 - Error Metrics for Skewed Classes (12 min).mkv* _6 N5 g# v# \7 {
    11 - 4 - Trading Off Precision and Recall (14 min).mkv+ u+ D; w, w! p. |' \# c
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a. Q: t/ [2 J# P- A* n4 L: @+ w
    12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
    % G! f* q! E" X- @+ w- g% r12 - 2 - Large Margin Intuition (11 min).mkv
    - g- S' |3 {- Z9 q- r12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv. K! {5 {1 _% E1 u+ j+ |  H  A
    12 - 4 - Kernels I (16 min).mkv  K0 X( h5 G, F6 m6 s. ?
    12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M
    0 v- A9 v% \( y7 v2 C% E/ Z$ \1 Y12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j7 |) d3 F8 P$ S. N; S' F
    13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    . m8 k, o) P) o* P6 _7 R13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    $ @' `* A& t  z0 L: x" r* L! i! {: V13 - 3 - Optimization Objective (7 min)(1).mkv
    3 ^  _& ^$ j9 h6 J3 t/ J13 - 3 - Optimization Objective (7 min).mkv
    ) ~3 Z+ B/ N* w# o' l; `+ `4 `13 - 4 - Random Initialization (8 min).mkv3 K0 C& Z+ |8 [' i, [; J3 K7 [. L0 b
    13 - 5 - Choosing the Number of Clusters (8 min).mkv9 N! Q$ S1 }/ K
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q8 v% ~+ k' I% _. N0 v& G. O- y/ I
    14 - 2 - Motivation II_ Visualization (6 min).mkv: s3 d3 C/ \# s* Z+ j  {: S
    14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
    / [4 B1 X% \+ p0 D1 @14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [
    , q+ y( l9 s' v* p& r& E) g14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g5 \3 r6 `% C# Q5 }, m
    14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @
    * K8 a% B& ?9 U/ U  v" i0 n14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    7 b) z! ~% M) O- g$ A15 - 1 - Problem Motivation (8 min).mkv
      R1 Y. L4 g0 T0 m; Z15 - 2 - Gaussian Distribution (10 min).mkv
    ! L* \( b' {5 N; M* g$ S* c7 M% B15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A! y  V! ]5 d! h  Q+ o  I( h8 e1 p
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    & w: j2 Z5 p! I  d0 B1 }. \15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
    ( }  V# f# ~' V8 ~/ e/ y) S) G15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q
    7 [2 p# j6 o% @3 p/ c) C- m15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U0 \9 @$ ~7 `1 |4 d1 y4 h
    15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e2 f( o5 |2 L5 o" G
    16 - 1 - Problem Formulation (8 min).mkv
    1 w+ ?7 Q% l8 z5 U; \6 K+ F16 - 2 - Content Based Recommendations (15 min).mkv' s- `; ?0 _+ v" e, K" q$ ~
    16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e
    2 I; x5 A8 K, v7 s- N# X16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
    / d- v8 q) m! w3 \- F5 `16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
    3 Q6 [+ Q+ [+ C+ k16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv4 R5 b$ u$ M4 e+ `* p  L5 @
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R% B7 Y: P+ c% N8 T* T- X! j6 W
    17 - 2 - Stochastic Gradient Descent (13 min).mkv
    - O6 T5 D( l/ x17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
    / T9 c8 e# d7 ^: s. K17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
    8 F5 i4 v9 n0 j5 N4 L" j1 r17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    5 L9 W9 _# O0 \! v; j17 - 6 - Map Reduce and Data Parallelism (14 min).mkv+ l( K3 M$ d% n3 ^! f
    18 - 1 - Problem Description and Pipeline (7 min).mkv0 @5 j$ D; F2 P6 u- k5 j
    18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    : z* B6 R; K/ @" P+ `8 i18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L3 T! i8 ]# ]6 d
    18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv( ~, [, i& ~$ J8 t0 F6 Q) l  n
    19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v* f% |! O% T/ f/ S. T' z
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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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