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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
    + z# o7 G% a) {7 ~) g6 n: ~1 - 2 - What is Machine Learning_ (7 min).mkv
    * o4 x, \4 ?+ c) `3 I1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    . L% H9 @1 b, T: D1 - 4 - Unsupervised Learning (14 min).mkv
    5 b% V7 s9 b7 }) i2 - 1 - Model Representation (8 min).mkv
    & `% J9 S; ]$ v& l: z) ?: w2 - 2 - Cost Function (8 min).mkv
    $ h% \  x- w1 W% w% o2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |$ m8 T8 h/ d/ W  y, S. G/ m; @
    2 - 4 - Cost Function - Intuition II (9 min).mkv0 T. i" h+ C0 U2 y) o' \) B( d
    2 - 5 - Gradient Descent (11 min).mkv
    4 X- P: a- ^* A3 @2 F! l; L2 _2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    ' w/ ~7 |- t- y7 ^9 E1 Y( e# I2 X2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |% z, ~2 n8 |2 G5 t2 }
    2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b1 C2 P& `( x0 H+ k3 D; e0 A' G
    3 - 1 - Matrices and Vectors (9 min).mkv( i! q1 ^; O: A( ~& F% N. I
    3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    & B- i0 C# x5 t5 H' J3 - 3 - Matrix Vector Multiplication (14 min).mkv
    & F/ D. N- w! c: S. E( e3 - 4 - Matrix Matrix Multiplication (11 min).mkv
    8 m, A+ z1 H5 K8 D9 {6 Z4 \" Q3 - 5 - Matrix Multiplication Properties (9 min).mkv, n  d, r4 R" A% Q# u* H
    3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    8 O1 g& R% W- n3 V1 S4 - 1 - Multiple Features (8 min).mkv
    1 d7 `1 O/ w" ^( X& t4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv! q% H/ ]* Q2 A8 ]. K9 I! e! ~
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv7 l, u: J; T+ o& z; w2 l4 \! Q
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
    ) G: m3 M& s9 ^2 f4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m
    " o' E; w8 K( x  n- m5 m4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X5 o( d& h6 T1 u# H/ I, L. u
    4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    ! ?0 P/ Q4 o8 H4 ]! U5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F2 y0 d6 C* l) X( J# b* h/ W' B: {
    5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N7 y# j; E6 g7 d/ S5 M
    5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z* U" }) @8 C  N( _8 X
    5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g$ a$ ?- R# d3 j6 D( z1 \: `! K
    5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u
    1 k, `6 v1 {8 ^5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \
      F  f4 _' E! g4 E; C5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y& v% O; s- k  l. A+ v- w
    6 - 1 - Classification (8 min).mkv  k( P  V4 {) O
    6 - 2 - Hypothesis Representation (7 min).mkv( `" q  i5 d( Y
    6 - 3 - Decision Boundary (15 min).mkv* N% z, t! Q; |# h7 v0 p0 N
    6 - 4 - Cost Function (11 min).mkv
    ! r) `/ H1 Q) D: v4 k) X/ w. ^6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y2 z$ t0 @0 L/ \7 `3 B% Q
    6 - 6 - Advanced Optimization (14 min).mkv
    0 d  R8 f# V. r* u$ I6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z8 j/ q9 q+ D! U0 t6 z6 M# G% q
    7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z1 D# T9 c5 ?. `  X) @4 t7 ?7 t$ V
    7 - 2 - Cost Function (10 min).mkv
    " m! E2 o) U! J* G7 Y3 k4 `7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    % T  ^" K3 H3 M, N& ^7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A2 U4 |) C$ S- I% P+ m7 }$ \  \6 n
    8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e
    ' [! }( c* K5 ?6 d" M8 - 2 - Neurons and the Brain (8 min).mkv
    # s( D( y6 H$ G% ~8 - 3 - Model Representation I (12 min).mkv! U0 {* O- ^  N( D% \1 O: k; q0 v  y% v" R
    8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
    4 ]0 T9 Q" o9 E3 W' o( w3 g8 ?! {! j8 - 5 - Examples and Intuitions I (7 min).mkv, \& x# o0 @2 P1 V7 F4 G
    8 - 6 - Examples and Intuitions II (10 min).mkv3 i% q8 I$ I, A# m3 ^( S
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z
    $ ?( n/ F; j0 U) B6 ~+ i. c$ |9 - 1 - Cost Function (7 min).mkv( U4 i8 J) s, X5 E) o) q& |2 r  y9 O
    9 - 2 - Backpropagation Algorithm (12 min).mkv$ I7 g# ]* p8 I9 q7 b- n8 y/ v6 v
    9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @, z2 j8 H9 ~- u
    9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L, B  z9 W- A, E9 I/ e3 X
    9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U' D5 _2 C' b' y2 ^2 h
    9 - 6 - Random Initialization (7 min).mkv8 d) }2 Y8 X. M! v- d# {& p
    9 - 7 - Putting It Together (14 min).mkv
      N9 P2 d0 O7 z' Q9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E' @+ U* a! i9 ^; ]* M* `4 i- I9 z
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e
    8 f- P4 M- S3 F! O10 - 2 - Evaluating a Hypothesis (8 min).mkv
    , z1 G/ d  o  _10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv7 \1 w+ E( l5 `3 J- ?* d9 e, ]$ H1 D
    10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G6 l( V$ q2 b# ]" Q+ p
    10 - 5 - Regularization and Bias_Variance (11 min).mkv9 s7 b7 t2 T2 }" v
    10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M
    . p6 Z- l8 F0 y10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
    & m; j6 X; l+ h* O11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    4 e( Z: H4 P. J+ Z0 K11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v/ p' N( H  K& G" ~, `! M( K
    11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
    : A- B/ k) m% D3 C8 M! l4 W; R11 - 4 - Trading Off Precision and Recall (14 min).mkv" m5 e3 n$ ~' O1 G+ n0 d
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a% Z* ]+ S0 A; ~; z+ n' f6 d
    12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
    " N; s+ s6 k; Q12 - 2 - Large Margin Intuition (11 min).mkv
    * m5 K7 k/ n2 w12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv/ P9 \$ m. y$ N1 H3 |/ s, }1 e$ D
    12 - 4 - Kernels I (16 min).mkv
    8 O8 A9 H+ ]% x: Q0 c( M12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M" j& \" H' T' j! m% ?/ E
    12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j
    5 K+ X) f' w8 ?+ x! u- q- M13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    3 ]0 T( f9 V6 B% ~' U. @0 w13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    9 |. q6 }2 {, i- A! S, X13 - 3 - Optimization Objective (7 min)(1).mkv- S0 A: G8 H9 ^: J9 N9 q. ^
    13 - 3 - Optimization Objective (7 min).mkv
    9 K2 Q& X' x$ e: `. S13 - 4 - Random Initialization (8 min).mkv
      G0 R5 m) y5 Z13 - 5 - Choosing the Number of Clusters (8 min).mkv
    - S8 I: a7 l$ N9 v4 q14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q
    . t* v& V. ^, M, v) V+ w* P3 N14 - 2 - Motivation II_ Visualization (6 min).mkv
    2 v5 [+ @% u6 f- v& f& @  k) t5 G14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
      L# V3 f7 f1 l7 }' [( h14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [7 X! b2 f; D9 q' q! O/ N: w
    14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g
    4 J7 \% v7 s0 O+ J+ m14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @
    : k  Y% U7 R9 C3 p  X14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    ( d: K: {4 g" p. B9 q1 v! _15 - 1 - Problem Motivation (8 min).mkv
    & X, z4 J* S. L# s" g15 - 2 - Gaussian Distribution (10 min).mkv
    3 t0 @$ h, G! i1 l! I1 C15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A! c! X5 E5 J8 e8 X4 }
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    0 `4 O7 l. {2 U5 Q8 N# @9 o8 g15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv( R! i4 g5 i: [: ?2 ?' m
    15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q- u( y: ^6 S. Y( }& d$ M  j# d
    15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U5 Q0 ]' p4 Q2 x. G9 ?# q- o9 _
    15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e
    4 t# G. h- F8 A+ {16 - 1 - Problem Formulation (8 min).mkv4 o  A' V4 Y; S" m
    16 - 2 - Content Based Recommendations (15 min).mkv
    2 @* h$ @, ~1 @& E3 y5 `/ @16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e3 h- |7 Q' m( \9 P9 T  h- {
    16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
    , ^* J' {% \2 ?" ?8 O# C16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
    7 `: Y! K4 _$ |, X16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv1 b0 H" ^+ R3 |
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
    - U# `( z" P$ x9 Y7 t3 E1 R17 - 2 - Stochastic Gradient Descent (13 min).mkv/ M4 B2 c& K" j& L* G
    17 - 3 - Mini-Batch Gradient Descent (6 min).mkv. _" r/ k' ?( e8 c% L
    17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
    0 R" ~1 F& K" s2 H17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?( J6 o. r, l- M' q+ c7 v2 C: T  h) T
    17 - 6 - Map Reduce and Data Parallelism (14 min).mkv( |* \* Z0 o+ C, r, g0 g
    18 - 1 - Problem Description and Pipeline (7 min).mkv
    ; e5 s7 W" A$ V4 V18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    / \& p& Y0 ^5 \0 y' L6 r! z18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L
    3 r3 H0 ^! s3 {18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv+ S$ V  U/ U  D- W  ?: P  ^
    19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v, l" P( _  i. U) 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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