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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
    ! d% m# _: o# U7 J1 - 2 - What is Machine Learning_ (7 min).mkv
    0 y% A1 _2 L% L- W7 Y1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    / h# t' {& Z) H1 - 4 - Unsupervised Learning (14 min).mkv% E: C) f0 n0 o( J. H( Y6 _
    2 - 1 - Model Representation (8 min).mkv! J+ b+ l. y* w2 T. M
    2 - 2 - Cost Function (8 min).mkv
    + _1 z$ x# a( x% @, b" ^2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    ; o* F$ p- l6 M$ y& f2 - 4 - Cost Function - Intuition II (9 min).mkv6 s* [9 M- o# E3 V6 o% F% ]
    2 - 5 - Gradient Descent (11 min).mkv
    ' D" I3 z9 D5 D1 a8 T; ^2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    ' [8 ^8 W. J4 p8 u7 [6 x9 @8 f9 q2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |0 W- Q; n- l- w; F% v( E/ _
    2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
    $ S) u" w9 n, t2 b5 b5 l3 - 1 - Matrices and Vectors (9 min).mkv
    / E: C4 n/ i2 S! z! _" O3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    ; |( L% {3 G5 p- Q7 T3 - 3 - Matrix Vector Multiplication (14 min).mkv
    / f9 e9 q0 ?  H7 A! D3 - 4 - Matrix Matrix Multiplication (11 min).mkv& S- ?2 J2 o7 n: \
    3 - 5 - Matrix Multiplication Properties (9 min).mkv
    0 _: W) v1 O6 N3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f) Z. U: S/ F/ M$ V  v
    4 - 1 - Multiple Features (8 min).mkv4 [, V$ T. i8 ~; ^4 s# J
    4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv$ m$ }: n) H  f
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv8 \. f8 w& t! w1 B2 S0 U
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
    * q1 W. g& o' E8 S5 {, p4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m. l3 t. m. f- a; h, U1 H6 i7 p! J
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    8 m9 k. c" h7 L. L4 Q# y0 Q6 J4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    ! {) |) w( X6 U4 P  L9 y5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F0 x$ C6 @1 M6 L' C$ c& j
    5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N- D% _% A7 p8 m
    5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z
    0 P" f. _  X3 o5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    5 b3 W  e% q. g: P6 h) I5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u
    ) E1 S4 g3 Q( U! [5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \: g3 a3 U1 L/ @* B' _5 `% w  F
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y
    : c* E) e1 y6 c) `2 z! m3 [( B6 - 1 - Classification (8 min).mkv
      |6 b4 c; V+ Z$ l6 - 2 - Hypothesis Representation (7 min).mkv
    3 [3 r* Q$ @: d, l( ]% d: J6 - 3 - Decision Boundary (15 min).mkv- I: }9 q. w! ~
    6 - 4 - Cost Function (11 min).mkv% @* k8 O- x2 P% Z7 m4 s
    6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y& j" T4 B& u$ m) b- e: N
    6 - 6 - Advanced Optimization (14 min).mkv
    7 `. p) D6 g: x  T2 ?6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z) S6 i9 U5 O* T% s. ?! o0 ^
    7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    6 F7 v4 L; S" I9 V  n# i7 - 2 - Cost Function (10 min).mkv
    6 P5 ^  f2 }4 \$ ]2 t) i7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    : H. M* D* h/ H+ }+ z: S4 Z7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A# n+ [8 A# {: P! e1 A  l2 c. f: f
    8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e
    ! i- `3 `$ f1 P. S" R8 - 2 - Neurons and the Brain (8 min).mkv+ `# Q) i. m8 G
    8 - 3 - Model Representation I (12 min).mkv
    : l5 Z6 G9 W' @. l( A& {" s8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H% h/ Z$ a7 {/ i/ R
    8 - 5 - Examples and Intuitions I (7 min).mkv7 O9 Y2 f! Q* l8 D3 P
    8 - 6 - Examples and Intuitions II (10 min).mkv$ Y0 |6 d1 I& \& S2 ^4 @
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z/ g3 `# P$ a" }/ o+ H. @) s2 W8 Z
    9 - 1 - Cost Function (7 min).mkv
    ! g& U& M' L( _7 {9 - 2 - Backpropagation Algorithm (12 min).mkv7 v) S( Y3 W1 {
    9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @
    # y( o6 I8 [. R, ]+ p9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L, H- q6 L: P. q+ W
    9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U
    $ |" U4 G5 R# a/ M' Z9 - 6 - Random Initialization (7 min).mkv
    4 @6 K! r5 m  X; L3 H4 N9 - 7 - Putting It Together (14 min).mkv2 J1 M: o' q" t# Z% A0 M: x: W
    9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E
    - v3 |# t9 m1 w  g5 Z9 W3 a3 N/ k10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e5 a% N/ y- G- ~
    10 - 2 - Evaluating a Hypothesis (8 min).mkv
    2 b7 G% p3 F5 J8 X7 Z1 m. x10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
      K; `$ P; d/ j10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
    4 z+ ?4 w; {0 s$ y) T7 d; W* H5 k10 - 5 - Regularization and Bias_Variance (11 min).mkv
    5 A6 K9 ]4 Y/ s3 I) ?+ S3 C10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M
    4 M1 ]$ q) Z2 F10 - 7 - Deciding What to Do Next Revisited (7 min).mkv1 `# b) `7 J- X1 \$ ]8 Y# M# g
    11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    , k, L- x8 P! K' D0 \" R. g11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    1 \; S, `. ]1 o4 S11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
    * S: r3 H  ~. }11 - 4 - Trading Off Precision and Recall (14 min).mkv8 u% Z! i# W! D
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a
    9 l8 @. S4 G6 D  r, i12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
    3 T% A$ S7 s' B& o6 o12 - 2 - Large Margin Intuition (11 min).mkv. D0 S7 x& A+ S, A
    12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
    3 m0 ^: R7 Z; ]0 O' q12 - 4 - Kernels I (16 min).mkv7 E" A, M5 h; J7 s7 L- H$ H
    12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M2 A# ~0 V# g, W; \3 I# M7 v- G
    12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j6 P. _; E! @, W  p3 P( q' P
    13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    + ^8 t2 `5 Z+ j3 P- C; {13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k) N1 h# ?; p' u9 K. ]: h5 }
    13 - 3 - Optimization Objective (7 min)(1).mkv
    3 L9 F9 g) g$ V7 F. ?1 Z13 - 3 - Optimization Objective (7 min).mkv
    - [9 B/ p  Y- {3 F/ `9 b& t) Y, G13 - 4 - Random Initialization (8 min).mkv4 Z2 a. v& H) Z3 Z( @
    13 - 5 - Choosing the Number of Clusters (8 min).mkv
    $ \( L$ r& S/ p+ u0 V5 B14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q
    2 I" R- |. S, w" P14 - 2 - Motivation II_ Visualization (6 min).mkv
    ! g2 i% b7 N7 ~7 ]14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv4 ^$ X. c! G$ m  _$ r  Y# U2 K
    14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [
    ) C% t" u  B7 q1 f14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g
    " l/ k! P* M+ L+ ~0 ^* W% U14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @+ [3 g2 f+ j2 x9 i5 a
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W
    4 V. m; @  [9 M" a4 D3 k- x15 - 1 - Problem Motivation (8 min).mkv& M% D8 x* h* t0 H3 d
    15 - 2 - Gaussian Distribution (10 min).mkv  p& E) E5 G9 o5 K: m
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A
    - `( y3 ^' R: [15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv6 y' T+ `6 q4 Y$ w6 e
    15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv& P5 k( C, P& b% p  w
    15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q0 r3 s: e: l3 c' V/ k7 [
    15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U0 v+ p% \3 V- Q+ G
    15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e
    ; c0 K& ?& o7 M4 b16 - 1 - Problem Formulation (8 min).mkv0 F$ v" W+ [; h. f. G2 C
    16 - 2 - Content Based Recommendations (15 min).mkv2 P4 H/ X" b$ g8 N
    16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e
    " h: ^) d4 i7 U# e* S! Q- I. C9 C16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
    . V% g% @3 s* ]) Q# }: w16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv. e- o& o# Z7 }. ]1 _8 G
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv1 b6 {2 E, @, W% H0 Z% y
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R& R% ?% Q. T, I+ Q- j9 }6 P( s
    17 - 2 - Stochastic Gradient Descent (13 min).mkv0 M- u& z* f8 q1 L0 m9 j2 K1 K- X2 Z" X# f
    17 - 3 - Mini-Batch Gradient Descent (6 min).mkv. ^  r! M# o' R
    17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f% ^3 r% A7 y0 n, A0 N6 l
    17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    6 v! i  h" O3 z, R17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
    2 D: B2 D; e3 I7 J0 G! \9 ?18 - 1 - Problem Description and Pipeline (7 min).mkv
    ; g) g: f, g* l" S! }" ?9 H18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d
    % J2 }" o* E2 y2 u% X" C18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L! Y6 `: N  c" V- P5 u
    18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    / l, I$ [. O7 L. T+ l0 t6 r- T5 ^19 - 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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