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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
    9 U  T- c+ r) k4 p. |/ [+ S1 - 2 - What is Machine Learning_ (7 min).mkv
    2 A  b! _/ _1 D6 J1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
    / n3 e9 b5 W) D! \3 o" P1 - 4 - Unsupervised Learning (14 min).mkv# H) @# B9 l  E. K% @, h
    2 - 1 - Model Representation (8 min).mkv% U# _- O& [& L1 v
    2 - 2 - Cost Function (8 min).mkv
    4 d: G2 d! O' v2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
    * W) O7 t/ F& }' |/ W7 Q2 - 4 - Cost Function - Intuition II (9 min).mkv
    ) z% S) f8 l9 l! w. }2 - 5 - Gradient Descent (11 min).mkv
    ( }# g; z, ?3 v, H! K6 V2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
    " @* I! H: g, x" E2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
    ; V7 Q4 J1 g0 C5 `3 d# ?, o- }8 S2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
    0 f; r* t% R; F+ x4 ]- B+ E3 - 1 - Matrices and Vectors (9 min).mkv
    # F6 }5 y  e- n+ z& Q3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?
    $ R( t8 a7 c) d3 - 3 - Matrix Vector Multiplication (14 min).mkv$ n4 Q9 w5 }, w1 f, I
    3 - 4 - Matrix Matrix Multiplication (11 min).mkv
    ( C/ ?2 O# G  W; n. {" [: Q. W' ?, `3 - 5 - Matrix Multiplication Properties (9 min).mkv
    + `, n( W! T- e1 t8 {# ^3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f& d# ?2 O" Y" N" W  B1 M$ E
    4 - 1 - Multiple Features (8 min).mkv
    $ E7 w) I2 ?& e" J8 l, t4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
    * I( V7 l8 R' L) j# P  a4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv; Q* P2 \( f; W
    4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv/ P" k. M% ~: `, J8 S
    4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m9 @6 c& @2 g0 D  }0 s7 z' |
    4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X
    5 U" I  P  a4 B$ D" x4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
    : D5 x- \. B' Q9 h! O! V5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F7 p8 D, A  |2 f; H1 W$ j, h
    5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N
    . k: F( P! k6 R4 `1 x; i  t5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z
      `& ?! Y; B$ x: E- Z( k5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
    ) j% H! X7 p1 ]4 p6 z, t% ?5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u
    ! E0 u, H! \; @) n. c5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \3 G! {  o2 E" g! f  _
    5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y7 }& h4 ~: j6 @5 o8 S
    6 - 1 - Classification (8 min).mkv/ ?- R$ N" X2 k8 I* L* g
    6 - 2 - Hypothesis Representation (7 min).mkv' c7 b  X0 }) T
    6 - 3 - Decision Boundary (15 min).mkv6 Q- ^; ?; W1 `6 ~) |, z) E% _
    6 - 4 - Cost Function (11 min).mkv7 x! N( p5 ]3 [
    6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y! w+ _9 R4 a* A4 M2 B
    6 - 6 - Advanced Optimization (14 min).mkv" p: r  Q) f- w
    6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z+ q  X3 h8 ^  _, j( `
    7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
    6 ~/ D! {% N* ]0 L1 `  V7 - 2 - Cost Function (10 min).mkv( [, j8 m/ S8 q/ W. o5 l& a' y; Z
    7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w8 k2 ?) M& K, j& L- P% ?
    7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A
    $ T6 H4 @3 S# w- |% U- R3 d( j8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e4 {" s  F: o) G. T0 ]' d) v; Y
    8 - 2 - Neurons and the Brain (8 min).mkv# z* P5 I2 U% a) |8 P6 b
    8 - 3 - Model Representation I (12 min).mkv
    ) O4 e0 I1 V2 {9 D8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
    ' |* H7 x/ G* a0 t( v- n+ d8 - 5 - Examples and Intuitions I (7 min).mkv$ x/ R% i5 Q3 J1 S9 K. R2 l8 T# Q
    8 - 6 - Examples and Intuitions II (10 min).mkv1 o' Z6 B; Z4 ?" z6 i. `" V
    8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z# ?  r0 H# U6 v1 T8 Z, R2 Y: Q
    9 - 1 - Cost Function (7 min).mkv
    % D0 v0 q2 X2 o, S2 e8 y9 - 2 - Backpropagation Algorithm (12 min).mkv
    9 e2 R  n6 j; V9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @
    . U; t4 f2 @! X' G9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L! n, {+ R, ^5 r1 U' [) J( a
    9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U5 ]/ E" X  Q/ Y% a: H: o
    9 - 6 - Random Initialization (7 min).mkv
      Z1 C- @- H. o) R6 c9 - 7 - Putting It Together (14 min).mkv. Z" |% b+ s; P
    9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E1 O% t; a# E3 q( I/ E& N# `7 B) R
    10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e6 B' N- g' e" U# K
    10 - 2 - Evaluating a Hypothesis (8 min).mkv! |. O. I% c' ^$ k4 O" a
    10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
    7 u; Z- M1 y/ u5 L  F4 u* w10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
    9 N, S; M- [5 K8 c1 a10 - 5 - Regularization and Bias_Variance (11 min).mkv
    & b7 q: v' b' L& _2 Q( T10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M
    . J" C, R! T; |) c/ n; I# I10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
    * q1 a" X0 K" B) k& ?3 }6 Z% Z11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
    ) X2 ^" w6 {  @" B& _# ?) Z11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
    ) M- ]+ ]6 i$ f7 Y, l11 - 3 - Error Metrics for Skewed Classes (12 min).mkv/ B3 ^" S: J! `4 D. b( W8 Y
    11 - 4 - Trading Off Precision and Recall (14 min).mkv  u2 b3 [2 a$ `' o" p, K1 {
    11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a
    ! M  W7 n. R! Q# D/ M: h" @12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
    4 f+ D! ?' I) X12 - 2 - Large Margin Intuition (11 min).mkv
    8 u3 @+ l9 E- N, F% O12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv2 s7 _" F- J3 y
    12 - 4 - Kernels I (16 min).mkv& }) \' x$ b! s' |+ J: q3 W; v+ ~
    12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M# H; |! K6 y( P: M8 d9 [& C4 H
    12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j
    6 s# d0 S$ V0 s' _+ z: `% T, ]13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d; L9 _; _. n  F9 |% d+ x- D
    13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k
    ) {& y( B* b% C- r$ p# I' M13 - 3 - Optimization Objective (7 min)(1).mkv( w8 B0 L; [5 r, }( T& L: \
    13 - 3 - Optimization Objective (7 min).mkv
    8 {' Y6 Q0 O- `5 M- f13 - 4 - Random Initialization (8 min).mkv
    7 Z2 b( Y8 s3 D13 - 5 - Choosing the Number of Clusters (8 min).mkv* A$ e3 G4 L& x, w% ^
    14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q
    9 R" e( s( `* y14 - 2 - Motivation II_ Visualization (6 min).mkv
    " E* W' y! H- X$ ^6 w14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
      r" n( R: h; v* h' R14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [
    + C+ R9 i- G: c6 p7 O6 K$ x/ ~4 p( O14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g1 d, {8 L8 h% n9 @# f5 E7 z
    14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @% {8 f: E7 D" {  M: a
    14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W) B6 i% @; Z7 T% z. u/ I
    15 - 1 - Problem Motivation (8 min).mkv; A2 c2 t, R6 d! g9 k3 C
    15 - 2 - Gaussian Distribution (10 min).mkv. J$ J8 G% t7 q6 {# e
    15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A
    + v# _8 Y1 D% g- y. C4 K15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
    + h; q4 k# o- e" R7 D9 O+ g& D5 R- }$ N15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
      J. j: r# S5 m2 a% ?9 l- w  a15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q
    - y3 _" {" K# D9 ^0 I* l. i15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U, U" k9 ?( @1 Y, V7 k
    15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e
    0 F( E9 S% {; X5 I) M0 R16 - 1 - Problem Formulation (8 min).mkv% {, \) o9 r  b9 t5 b1 O5 ]6 f* p
    16 - 2 - Content Based Recommendations (15 min).mkv
    6 s4 b  N, K3 A( m- U$ J. U16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e: q2 I& a7 T7 g$ L( z, v
    16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]" w& ^* c& r& @3 Y
    16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv3 y5 i& S, i; }( X2 w8 {& G1 x% V
    16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv/ ?- J% ~* R- s0 {8 C4 G
    17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
      w7 @& X, p7 X) U( H; j' T' s  J, v17 - 2 - Stochastic Gradient Descent (13 min).mkv
    : x- d( r3 G  Y. l9 ~17 - 3 - Mini-Batch Gradient Descent (6 min).mkv9 \& m3 d5 w* \" u
    17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f5 S8 b+ z! W; X( {
    17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?
    3 N, A% t/ o: n. s& {" s. W17 - 6 - Map Reduce and Data Parallelism (14 min).mkv/ O8 j! ?0 e( A! J) Y
    18 - 1 - Problem Description and Pipeline (7 min).mkv9 s: `5 d& R, b  T6 f+ h
    18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d- z0 }" g! _0 B' L
    18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L
    0 Q7 i+ g: ~% |  u. {$ X18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
    0 A- ~, X% M& t& a, B19 - 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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