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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
    ! h, f( \3 C* v5 D* Q+ Q8 ]) T1 - 2 - What is Machine Learning_ (7 min).mkv
    ) j& u/ A1 f2 B, c. L1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    " D% r( g, s$ @3 U. E% p1 - 4 - Unsupervised Learning (14 min).mkv
    % D0 n7 K. Z4 z  u( W( e$ ^2 - 1 - Model Representation (8 min).mkv
    7 K* K+ C1 [0 W2 - 2 - Cost Function (8 min).mkv! R  V* \! A: p3 e. @3 R
    2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    % e# K! p4 V: }" a2 - 4 - Cost Function - Intuition II (9 min).mkv
    1 H0 L& A! r2 ?) _* u# @4 j2 - 5 - Gradient Descent (11 min).mkv
    0 X4 W, T9 g: |0 ?" S  w2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p. m" `% Y* O6 }5 [/ s( o
    2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    ( m+ t* \( z+ M$ R$ G2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
    7 G% L- z5 x1 X! W& z1 L0 t% m3 - 1 - Matrices and Vectors (9 min).mkv
    - f$ j  m( G0 {3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    . `) X1 a( m( q  p; f3 - 3 - Matrix Vector Multiplication (14 min).mkv7 r$ D7 T4 E$ S3 `  a; A
    3 - 4 - Matrix Matrix Multiplication (11 min).mkv7 i6 A3 [; h+ M8 U) k  `7 U  q2 y
    3 - 5 - Matrix Multiplication Properties (9 min).mkv
    - H1 t- N  R' z: R- X3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    + k5 |, q! d$ m: }4 - 1 - Multiple Features (8 min).mkv+ @4 ?* J1 j+ e5 N1 c$ }5 w- ?
    4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
    & W8 j6 D: S& O1 z) q0 ]$ j! t  @6 [1 O  M4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv/ N* H1 ?# m4 J9 D
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv( A6 c1 d7 u% y- V
    4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m& Q) P* V( p4 ~5 ]  g3 d4 W
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X& k/ ~0 Z; w6 G  g
    4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |* u/ F5 x1 h! B+ k: Y2 i$ [' g3 N0 R
    5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F
    # K9 L/ L3 H& D  e5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N& J6 f8 x9 y- A" U) a
    5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z/ `3 q: V; L9 z, I
    5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g8 I: f2 e* l" q1 F0 U- L+ e! F
    5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u* {. z, ?7 d9 E; Q
    5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \) h) B6 a3 [5 x0 Q: H7 O7 x4 j  ~
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y! p$ ~" |: A3 h( g4 i1 _2 L
    6 - 1 - Classification (8 min).mkv
    5 R) f9 h; H# o7 Y0 `. G6 - 2 - Hypothesis Representation (7 min).mkv
    2 L0 B# _  M  H6 e2 f: w6 - 3 - Decision Boundary (15 min).mkv
    + r# D1 l0 r! O# L6 - 4 - Cost Function (11 min).mkv
    $ w; q& o" t. `  G1 W6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y
    0 G/ ~! Y9 j' B% ]( C1 [6 - 6 - Advanced Optimization (14 min).mkv% j8 `1 P$ m! p' B
    6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z
    , o. q$ R7 Q9 k7 g7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    ; M: I8 ?$ G- {, w, _" o% {7 - 2 - Cost Function (10 min).mkv
    ! x; y' ?  ^) N- c- i( j9 K, a7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    6 Y  `- M! s% z: A7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A. V2 w2 p  P  O7 ?- k2 a/ u( E
    8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e
    9 R, b; |) L+ V# U; E7 u8 - 2 - Neurons and the Brain (8 min).mkv
    6 g- l* K, W- |8 - 3 - Model Representation I (12 min).mkv- t  C9 t5 A& ^
    8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H& e3 E( P8 m# ^* [/ G( X
    8 - 5 - Examples and Intuitions I (7 min).mkv
    0 b% `" O' Z) I3 F  H4 H% `8 - 6 - Examples and Intuitions II (10 min).mkv$ c! w3 E- @& Y: n
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z% \0 O; |: R- C; D
    9 - 1 - Cost Function (7 min).mkv' O2 G6 W5 t# h  q/ H1 h7 i) `1 u
    9 - 2 - Backpropagation Algorithm (12 min).mkv
    . l4 `4 G% p! R& A- F1 `- o9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @- D/ E% h. v7 z' L# k
    9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L
    2 h; [6 u) f, D/ b" y* X9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U0 m$ u1 B& }" C* S' s
    9 - 6 - Random Initialization (7 min).mkv7 m# M5 P. o  v
    9 - 7 - Putting It Together (14 min).mkv! K- g6 J, M" u6 B; `9 }
    9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E
    $ W) k! X. [% L( i6 T, @10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e4 d+ O( v$ f' A4 Q+ p4 T% J
    10 - 2 - Evaluating a Hypothesis (8 min).mkv. `2 ^: T3 I5 G* e7 q; a' L
    10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
    0 M& ^! G3 ?4 Z, |1 A7 x9 q10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G9 F8 y0 S& L0 h) {0 N
    10 - 5 - Regularization and Bias_Variance (11 min).mkv/ F" L" o! C; V5 J5 Q: n
    10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M6 A/ M& Q$ T) W& l/ w/ Y
    10 - 7 - Deciding What to Do Next Revisited (7 min).mkv* k8 E  ~' N9 R
    11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m6 K, j- J' u" W4 C( ~- u
    11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    , {$ n/ i- C* j+ B  \6 l11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
    ' \5 O7 N6 p1 J4 J11 - 4 - Trading Off Precision and Recall (14 min).mkv" C" N$ B- U' c9 s/ s. @9 L4 H
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a; `- ^2 O! }" Q0 F- }2 j. v% s7 M
    12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c8 }8 c  i' p1 @8 q  P5 K# M* L
    12 - 2 - Large Margin Intuition (11 min).mkv& L! d, h5 e4 {* y9 C0 `2 ~
    12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv% i6 l* B5 x: H2 a' Y, @$ H1 c! Z
    12 - 4 - Kernels I (16 min).mkv
    ; r' w0 T5 y  j  Z" y3 u12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M9 k0 B2 e) j4 F0 L% f
    12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j7 y9 P0 S* T& k' P6 B
    13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d: _! X* P# V, }; \
    13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k1 A# s" P8 I6 o' R1 K. ]/ |! M' Z% O
    13 - 3 - Optimization Objective (7 min)(1).mkv2 M, P# \9 D  p2 f" z3 f& r! m( w( t
    13 - 3 - Optimization Objective (7 min).mkv
      u" D: a0 f" t2 ]7 z; o( J5 g0 P13 - 4 - Random Initialization (8 min).mkv
    % r6 l& z0 ^% B5 w13 - 5 - Choosing the Number of Clusters (8 min).mkv/ |1 F$ I3 g: J; e! m
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q% i% `7 m' C# a; h
    14 - 2 - Motivation II_ Visualization (6 min).mkv: B. W+ v( O. J8 v# n' S" W8 J% r; ?
    14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
    , l+ O- J0 @, ^* i% K& j, [3 `14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [
    + g' P, l) ^0 l- p8 W0 |14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g& i+ i1 K- V% A9 u1 H
    14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @, b' D  r' h9 Y* R, B8 T: \. j
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    + v; b- D* Y* T& v1 f/ B7 X0 Z8 @15 - 1 - Problem Motivation (8 min).mkv# [& {! g' G6 P3 X
    15 - 2 - Gaussian Distribution (10 min).mkv, G& M# m1 K( a# I
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A# @8 R. ?. E9 P1 t6 R/ i& G
    15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv. n3 _+ A- [, x# H
    15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
    ' T- W6 f, h, ?5 ]15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q. F# }. S& u$ b9 M! B5 w0 t
    15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U
    . b% C! @* W* o( G0 V1 R15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e
    . C  ?5 O/ r- x16 - 1 - Problem Formulation (8 min).mkv: r- n8 F6 e9 ?' G/ J
    16 - 2 - Content Based Recommendations (15 min).mkv5 W3 Y( E& `4 \" c0 b8 m8 m
    16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e8 Y" F! R2 t: |7 y- A) }( C  y2 ?: P
    16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]$ ]* f" B# S8 ^( p5 w0 o9 b
    16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
    ! h7 B. [) ^* z/ k" O16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv
    , ^/ m4 s0 H0 i8 R! l" G  n17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
    " A0 Y/ b! I  q9 l17 - 2 - Stochastic Gradient Descent (13 min).mkv
    ( H1 Q: u4 F/ j7 t; s17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
    * a+ g+ y# a/ {- a2 C17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f0 ]  ~3 ?$ V6 y* L. `8 M4 N: k  o
    17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    ' g$ Z( [! {/ |! Q+ Q. ]17 - 6 - Map Reduce and Data Parallelism (14 min).mkv% |/ U; D8 @3 C; b
    18 - 1 - Problem Description and Pipeline (7 min).mkv
    0 j8 S$ m% m. @. p) k* Q' z, u18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    9 e$ ^# J, p+ |# l$ b& }, i18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L
    + V( }; ]8 S7 j# ^' T% J  j18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv) _4 h9 }6 X7 o, @. l, N$ {7 }
    19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v9 d. E; l9 E5 k: v# E1 @- L0 d
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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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