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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
    6 P/ _! n2 V3 B  E' L0 }; K* K! ^1 - 2 - What is Machine Learning_ (7 min).mkv* L3 R( Z+ @5 Q+ p  D9 B
    1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    3 m/ j$ p$ ~  c' c) r: D3 F% @4 _& u1 - 4 - Unsupervised Learning (14 min).mkv
    & A# @2 L' c8 }: J7 l3 o6 C' y2 - 1 - Model Representation (8 min).mkv
    0 W4 q/ z! r2 ~6 V, N# N2 - 2 - Cost Function (8 min).mkv' K1 u; J8 U+ }( V* O; U
    2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    . u0 l. \( U/ V& `2 - 4 - Cost Function - Intuition II (9 min).mkv
    " ^# ^! v1 c" U2 - 5 - Gradient Descent (11 min).mkv
    1 n5 f! D2 Z' C% B% V; M2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    ( i5 b6 l9 ^9 I* [8 R2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    3 J% c* f6 g7 u- [2 H* Z3 I: L! k2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
    6 x4 S( @# L; W+ y3 - 1 - Matrices and Vectors (9 min).mkv: m; E! T/ k, }: H
    3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    ) H2 s8 ^! L0 T- y8 l3 - 3 - Matrix Vector Multiplication (14 min).mkv8 S! q  a, |) i; T, U8 b) y
    3 - 4 - Matrix Matrix Multiplication (11 min).mkv0 z- r' c2 ?4 j) ?) i8 {
    3 - 5 - Matrix Multiplication Properties (9 min).mkv2 {0 e. e/ B" _7 Q
    3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    ' w8 P& w9 l  k4 - 1 - Multiple Features (8 min).mkv; O5 a6 W' p% @( f' V  G/ }( x' o$ _/ z+ I. V
    4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
    8 m% d7 l, X' I& d: D4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
    0 S( p. R/ J1 @3 D0 l0 E5 {) q4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
    1 U9 }1 H( d# Z5 Y) M  m$ Y4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m8 O0 ~: M9 ~9 p5 c; Z( T0 b( |
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    2 }% U# t9 }# B8 P9 s4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |# J% E! j) F' [/ f3 f, F9 ~% A' U6 `
    5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F
    1 F" Q' t- L; U1 a" H0 J' ?2 p5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N+ i$ T- j& x' P" |# J! Z* X/ L) P$ y
    5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z
    % X& y& _$ D8 ^3 u5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g  B  p, B0 }$ g2 N: j7 ~8 ?* d& ^
    5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u* \6 J+ D2 z$ U: `
    5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \* D$ g( V% s* r5 _+ G
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y; |2 c* E0 i" H. Q/ ]0 R1 |% v5 R
    6 - 1 - Classification (8 min).mkv
    : g- X9 t4 e3 J( X- m9 X6 - 2 - Hypothesis Representation (7 min).mkv" F* N8 z: R  a2 b3 x
    6 - 3 - Decision Boundary (15 min).mkv7 b2 Q( `# Q( J: o
    6 - 4 - Cost Function (11 min).mkv
    % G& @9 d5 i, k) F: O6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y' n' ~5 w; k7 O, P* r
    6 - 6 - Advanced Optimization (14 min).mkv
    5 H+ n# V0 @3 h6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z+ V( X  C8 w0 T7 F! W
    7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    & x$ K9 G$ e! l3 s5 x8 X0 x# ?7 - 2 - Cost Function (10 min).mkv
    % i' \) j0 J7 j' \, c* D7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w: O6 A* t6 B5 P5 l: u
    7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A
    & J& q3 d: _( T- @+ X- W0 \2 g& N8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e6 g/ S7 }' W1 p1 R# M
    8 - 2 - Neurons and the Brain (8 min).mkv
    8 s; T- a2 D, k2 E& D4 N8 k6 O% R8 - 3 - Model Representation I (12 min).mkv
    / Z. F: S1 J6 B& O3 G  K. Y4 K% |8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
    5 m. j1 `7 W& I; Q1 _( H  s) z& x+ L8 - 5 - Examples and Intuitions I (7 min).mkv
    $ p: o6 n3 b+ J% {8 - 6 - Examples and Intuitions II (10 min).mkv' ~5 T. X! Z; [+ C- E
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z& {* p2 h- w2 s2 s) c" y+ `
    9 - 1 - Cost Function (7 min).mkv
    $ s+ N4 {! Q$ ]: t9 - 2 - Backpropagation Algorithm (12 min).mkv
    # @. }  }) p9 Y$ d9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @! a( b2 ^3 w6 z2 w1 K3 e1 }
    9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L
    . X6 P0 v; t, Z5 _. E9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U
    . K& g  _* n, g% d1 u. A7 N% H9 - 6 - Random Initialization (7 min).mkv
    ) Y$ o/ U& J8 s! t8 G7 y9 - 7 - Putting It Together (14 min).mkv' F) t3 G8 R7 Y  k1 ]" b3 |
    9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E, L+ Q9 q* B/ G& K( n8 e: T8 X
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e
    $ L! S2 b; G; z4 f$ [10 - 2 - Evaluating a Hypothesis (8 min).mkv
    ' _4 t. ~% `  s10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
    5 H! G; }& V7 n9 Z10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
    ; H, ?2 `& f' \10 - 5 - Regularization and Bias_Variance (11 min).mkv
    , ?7 ]6 i. J# |9 p10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M, M/ K$ V: k  A' a3 ?0 V" m, x
    10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
    + D4 U/ r; f, z5 z, A6 g0 h* z6 H3 }11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
      m$ P6 y* e% U2 ]( c11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    + t1 \8 s7 y% d! a0 w  i# P+ x2 H11 - 3 - Error Metrics for Skewed Classes (12 min).mkv0 N8 {. x; u5 ^5 c( x  Z' @4 {
    11 - 4 - Trading Off Precision and Recall (14 min).mkv% p& G. o1 y/ T3 Q& M
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a
    ( d0 A8 ?+ U7 R+ k3 O. |) ?12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c. t8 k5 ^1 T) Z6 F6 `
    12 - 2 - Large Margin Intuition (11 min).mkv
    5 ~4 Y) i! L) r( k9 }12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
    9 ?8 B( V5 N) y9 [# }12 - 4 - Kernels I (16 min).mkv. Y, L0 i4 _3 V: |/ }
    12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M
    ( a8 |# z& a, V9 \% u12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j
    , R" U. D( G% M2 [% ]/ g1 }% |13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    & J) P$ r; v$ h  a13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    1 v1 y  l; t; u$ ~13 - 3 - Optimization Objective (7 min)(1).mkv
    & n7 C; f5 z' ~' t: D9 C9 `13 - 3 - Optimization Objective (7 min).mkv
    . }$ c# I  h7 S* [* `13 - 4 - Random Initialization (8 min).mkv' x6 i* s! x; O  r& W5 [! Z
    13 - 5 - Choosing the Number of Clusters (8 min).mkv) k* G% V6 T- _0 U3 U0 m- A
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q  w1 t- u6 L. A
    14 - 2 - Motivation II_ Visualization (6 min).mkv
    ! ^; ^; H" O% v6 X14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv9 [6 ?' ]$ N% x
    14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [- _$ E' \& ^; {; A$ G
    14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g
    ' C: `2 L: X5 S6 u3 C! E14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @# g6 a/ m3 R! O/ m
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    . Q6 {  L5 p/ {! V15 - 1 - Problem Motivation (8 min).mkv
    ) o: T% L# t# h2 m15 - 2 - Gaussian Distribution (10 min).mkv
    $ F0 N5 D& ~. y! q* Z( U15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A3 B- B+ ^$ C& T
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv  J( k1 y% j; c4 [9 }
    15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
    1 G% _, r( f2 z# R. d$ [% t15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q, T! ^2 |& g, e$ o; _7 J: E
    15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U
    5 _, z* X" [# M2 R15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e9 w- _8 z$ r2 b$ }! k% K7 t0 @
    16 - 1 - Problem Formulation (8 min).mkv7 n3 ^# A3 i# D0 d! S
    16 - 2 - Content Based Recommendations (15 min).mkv
    $ Y# {6 z  d+ Y" q16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e
    9 \5 D7 Z9 e6 B; y16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]. e) h( ~; p+ ?2 T1 U8 m* _
    16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv1 c! k* f) K- r/ c$ R8 U; q
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv+ f+ D1 b# C/ T1 [, c
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R4 G* ^& X6 _& Z: f/ ]0 T# ^8 k0 S
    17 - 2 - Stochastic Gradient Descent (13 min).mkv! H! b1 ]  _2 w" ]4 Y) c0 P6 X
    17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
    ' ~$ L5 I* A" W1 p# Q; ?( E7 V17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f/ z  l9 H/ [4 |+ q! _
    17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    ) I$ L4 X- b0 [' N17 - 6 - Map Reduce and Data Parallelism (14 min).mkv2 J/ w# o, j% P
    18 - 1 - Problem Description and Pipeline (7 min).mkv2 Q1 e. E4 T" c. r
    18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    ( I2 A  g+ L  R: z4 ~3 _18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L3 Z% w1 ]# V* D; ~" N
    18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    / S- h) {$ \$ i5 a3 W. `2 w19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v
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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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