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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).mkv5 Y' _' Q! P9 g
    1 - 2 - What is Machine Learning_ (7 min).mkv
    ( F2 g* A$ _3 Y3 C* U+ \% u9 o* z7 e1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E$ U; l/ A! g9 U7 A# ~2 {
    1 - 4 - Unsupervised Learning (14 min).mkv* ~/ k, @. e3 j7 ~' G$ H
    2 - 1 - Model Representation (8 min).mkv4 O, H9 x, H0 L: q
    2 - 2 - Cost Function (8 min).mkv7 ?9 D3 b! `, P3 C5 m6 M
    2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |" h: f3 C2 Z" j% n+ ~
    2 - 4 - Cost Function - Intuition II (9 min).mkv
      [, u& q" c" r: e2 W2 - 5 - Gradient Descent (11 min).mkv$ n( ~+ z# x( V3 T5 `5 F
    2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    / B9 Y2 g  V. p- T7 F$ m9 K2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    3 E; d6 A# e; q2 _( b# y2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
    6 \: Z& w; a6 W8 r1 i7 [# W3 - 1 - Matrices and Vectors (9 min).mkv
    - K5 M, V# W. p9 I! E' ^3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    2 ~) `' L$ |0 V* U* }6 V3 - 3 - Matrix Vector Multiplication (14 min).mkv0 a+ z. y6 ~" t$ ?
    3 - 4 - Matrix Matrix Multiplication (11 min).mkv
    " \6 m, u1 |' S" j3 - 5 - Matrix Multiplication Properties (9 min).mkv
    " J+ c" j% r4 o& T& q* t3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f
    . Q6 K$ v* [8 c3 R! j2 K% r4 - 1 - Multiple Features (8 min).mkv
    ' z/ q. N- r  q" U! ~( h4 q4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv& q% w/ b9 c: i' {
    4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv  x; n3 R" S9 H+ c/ M' r% c
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv7 N" {, X% O8 \( P( v8 Z8 R2 h8 J. @
    4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m- @& n: A* @" i* q- b2 v: W
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X, s* R+ ^- u/ u, N7 T* d6 O
    4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    + e9 G- ?8 M5 y, g* h* R5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F2 ?7 G' E0 X  d4 u
    5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N
    * b! {8 V( O0 }5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z
    + Q5 ]5 N# L$ f% u# g7 ?2 W5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g! @4 S- d2 A" ?# `* z- ^; E
    5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u
    5 |+ D3 O! }' S1 O  V- i& Y* l' s5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \
    ( Q4 |/ }' c: _) E/ W5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y  c3 C7 K- }' W. X# a. z
    6 - 1 - Classification (8 min).mkv
    0 K  ]7 j6 u# n6 m6 @6 - 2 - Hypothesis Representation (7 min).mkv" J% M6 e5 V% j  p' @. s
    6 - 3 - Decision Boundary (15 min).mkv: w5 b: j* r4 j9 P8 N; W
    6 - 4 - Cost Function (11 min).mkv
    $ _' Q/ m; L; L7 Q3 A( D6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y' x. U* x* |! ^, w. c
    6 - 6 - Advanced Optimization (14 min).mkv# P5 z- O4 V) x- G3 R/ @
    6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z
    0 x$ k9 \7 M% K# S/ z9 W7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    3 y+ \- N/ d2 n' h7 - 2 - Cost Function (10 min).mkv
    7 `: x. }: m3 G  Q0 B* V$ u7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
    ' x4 e: \: R9 u7 Y" \/ n7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A
    6 I9 }) h' O# ?8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e% O+ G0 n' x0 K
    8 - 2 - Neurons and the Brain (8 min).mkv* ]+ X0 v3 b" \
    8 - 3 - Model Representation I (12 min).mkv5 o+ ?, |4 ^2 ^( R) q
    8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H) @, n2 f) l" k& _4 p& N0 d
    8 - 5 - Examples and Intuitions I (7 min).mkv' h' w( h4 y* @1 Y
    8 - 6 - Examples and Intuitions II (10 min).mkv% r9 L% z& s0 y
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z
    - }0 f' K# I) Z- N% T& d; r! V. b9 - 1 - Cost Function (7 min).mkv" j- Z5 O7 m$ Z; S6 I1 p' F
    9 - 2 - Backpropagation Algorithm (12 min).mkv
    5 H; f/ ?  U) T5 p4 i8 Z, U9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @* x6 t- Y# E9 T  v9 x3 t( I2 Z
    9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L5 @+ D7 w- r0 O. A/ D
    9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U, B+ ?" S9 W( [3 s
    9 - 6 - Random Initialization (7 min).mkv
    % k2 [  o0 c$ L8 h& _, [, `9 - 7 - Putting It Together (14 min).mkv
    6 _0 d  [8 a0 w+ A5 S9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E) n9 v5 m' L$ \6 ~1 ~2 u
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e1 E& Q& g9 e4 [) V. V9 o2 t
    10 - 2 - Evaluating a Hypothesis (8 min).mkv5 ^5 O: n8 B% y  [% O) c
    10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv* d3 S) @+ f, I4 |. L
    10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G! ]5 z( `6 k# z" c
    10 - 5 - Regularization and Bias_Variance (11 min).mkv
    ' H& f( ?3 u; v" F10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M
      a  ^3 c+ Y- I& r9 T10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
    & F7 X. [2 T+ Z% R11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    9 B4 k9 w+ c7 L4 P' t3 r11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v8 Q6 P$ {& w5 i" C! e$ d
    11 - 3 - Error Metrics for Skewed Classes (12 min).mkv, C0 H6 ?$ R( X3 p8 \; x3 Z* r
    11 - 4 - Trading Off Precision and Recall (14 min).mkv
      Y; E" \4 u; M4 H! p- N! {, a11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a. x1 Z# l' s+ j
    12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c7 v$ k/ n5 H9 l0 ?/ z8 u7 b1 h, ?& v% I" p
    12 - 2 - Large Margin Intuition (11 min).mkv3 i6 o1 N1 u" O3 i9 F( {
    12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
    % x% |1 A3 A5 [9 L: T. u3 E3 p12 - 4 - Kernels I (16 min).mkv
    8 f  a! j0 |5 W; T12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M
    # ~% O, e, t1 p3 F12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j9 d9 A) U( }. B0 H6 H0 c
    13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d
    , x  r5 A# Q, S0 h13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    1 h# O$ K. q# D, A( ~7 Y" c0 P# E13 - 3 - Optimization Objective (7 min)(1).mkv9 d. }: m- N/ W, `" X& R, w
    13 - 3 - Optimization Objective (7 min).mkv( z: r/ V$ u& p5 h
    13 - 4 - Random Initialization (8 min).mkv4 s0 t9 o8 g) ^7 I/ D/ l$ }5 W: f
    13 - 5 - Choosing the Number of Clusters (8 min).mkv
    . a5 a( z' ]8 Y14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q
    % N9 E6 b' B7 v$ c( d14 - 2 - Motivation II_ Visualization (6 min).mkv
    3 r4 w, p3 i, Z/ w14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv! f; T  L% k- J  c
    14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [* t4 L! h, ^' p# }
    14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g
      M$ Z3 J! n) @1 g2 U, [4 s14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @
    : C% I3 n( A* a; n/ a$ q% P$ u/ l+ J14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W2 S" Q7 |1 r, C% s& g& I! y
    15 - 1 - Problem Motivation (8 min).mkv
    4 H( D* S/ W0 W2 H15 - 2 - Gaussian Distribution (10 min).mkv) ?& T( s' }2 ^9 P1 {. C
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A
    - h3 v- }  Z/ D6 y( G  x15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    * K) c  e/ O3 P15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv4 g+ |) d% |/ T2 q
    15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q
    ( T( x5 O! o) D7 y( g4 A" A( C15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U
    ; {# E% O3 |2 Q8 w. M3 H15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e6 T& u1 B9 L7 h2 K8 {
    16 - 1 - Problem Formulation (8 min).mkv
    6 S( P5 q# E' J' z: h16 - 2 - Content Based Recommendations (15 min).mkv
    # ^7 g' r" W9 Z" i( w% k$ o( C16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e; T* q% v$ p, r/ K7 E
    16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]3 F$ P& e( _  y0 P
    16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv  y! s  ^+ B/ H1 J0 M% ~) N
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv
    ; Z: l3 w: u* r: w3 G17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
    ) X, p- [  t$ B! G/ K5 L' K' O17 - 2 - Stochastic Gradient Descent (13 min).mkv6 m" c7 n" y# {. F  |
    17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
    ; K8 t; l( \5 u9 X8 u17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
    4 n, `( w0 K$ u" k% f; U% i17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?2 j  s5 ?! D8 w
    17 - 6 - Map Reduce and Data Parallelism (14 min).mkv! ^, M/ V) {& B) e' D2 ^' O8 \8 {/ S
    18 - 1 - Problem Description and Pipeline (7 min).mkv
    & M6 y4 q5 p4 O, \& p18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d# `# \/ E1 k0 C5 b2 U/ f
    18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L
    : `" e* T! X* p7 Z  T18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    3 Q& F* F! l# k19 - 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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