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

余生 · 发表于 2019-4-10 14:16

1 - 1 - Welcome (7 min).mkv8 M' j6 U: g" W( k, ~9 ~( O( f
1 - 2 - What is Machine Learning_ (7 min).mkv
1 - 3 - Supervised Learning (12 min).mkv  Q$ o. \1 R; H: E
1 - 4 - Unsupervised Learning (14 min).mkv8 P7 X4 o7 W0 q9 r
2 - 1 - Model Representation (8 min).mkv9 q6 H2 {; l1 l' p8 z! D* g4 U- S
2 - 2 - Cost Function (8 min).mkv
2 - 3 - Cost Function - Intuition I (11 min).mkv! B/ S, s! ]5 a9 |
2 - 4 - Cost Function - Intuition II (9 min).mkv
2 - 5 - Gradient Descent (11 min).mkv
2 - 6 - Gradient Descent Intuition (12 min).mkv: q7 |& e: z) N; y! t7 _( D9 p
2 - 7 - GradientDescentForLinearRegression  (6 min).mkv, o% F, ?! f  @# Z1 J% f2 |
2 - 8 - What_'s Next (6 min).mkv5 C& c/ A1 o6 c4 b
3 - 1 - Matrices and Vectors (9 min).mkv+ l+ _; C+ b% c5 F3 R/ y
3 - 2 - Addition and Scalar Multiplication (7 min).mkv% P# t7 C! r# x; _% ?' V: U/ F+ M5 d2 p  ?' S. G# K) x
3 - 3 - Matrix Vector Multiplication (14 min).mkv- W4 {' t/ V  Z: T9 F
3 - 4 - Matrix Matrix Multiplication (11 min).mkv/ O/ [, _) L( n' e
3 - 5 - Matrix Multiplication Properties (9 min).mkv3 S5 g7 b1 x1 U0 w* d/ u: o3 f
3 - 6 - Inverse and Transpose (11 min).mkv5 q: h" v+ e  D; J$ P) J/ f5 M+ u) [6 C' B3 d
4 - 1 - Multiple Features (8 min).mkv
4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv! s0 m+ ?# p/ F' ?& h
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv+ x& e0 t7 p; K" |* b. ]  {
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
4 - 5 - Features and Polynomial Regression (8 min).mkv# A- u! R# V* l: q) p& R3 m7 Y7 y. A8 m# Z0 t% Q) U5 b
4 - 6 - Normal Equation (16 min).mkv! d: w( v5 J$ M9 _) l- X  F. P" D7 G& d4 A* k
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv+ x& H' R3 ], r* s3 q  |- |
5 - 1 - Basic Operations (14 min).mkv) }6 Y# a2 V) l( W' F# M& v8 g/ X7 z$ ~, e% J
5 - 2 - Moving Data Around (16 min).mkv: f+ m6 {' |, S  O) X% n! }) |) N
5 - 3 - Computing on Data (13 min).mkv0 k5 H* ~0 \1 ]- p2 z. b# g, M' z6 z: v
5 - 4 - Plotting Data (10 min).mkv) g- j: ^& L2 g
5 - 5 - Control Statements_ for, while, if statements (13 min).mkv  k7 m4 Y2 f+ J- m3 U7 m; u9 ~! @2 p& n, |( B% W  ^* G
5 - 6 - Vectorization (14 min).mkv5 A- @! s$ a2 y* x+ h) R/ |# \: o4 S8 z- I" s7 S
5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv9 {/ I$ V$ R9 z0 R' ^0 y
6 - 1 - Classification (8 min).mkv
6 - 2 - Hypothesis Representation (7 min).mkv& f2 T& H2 b% a1 U8 s
6 - 3 - Decision Boundary (15 min).mkv% H  c" ?# e3 o" X) J
6 - 4 - Cost Function (11 min).mkv
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 A0 L7 D# n; U' y
6 - 6 - Advanced Optimization (14 min).mkv" l  N" o0 Y7 E9 W5 a/ D" f& D& c( t: M
6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv& j8 @" n% _4 V' ]8 o7 G  k/ Z5 @# q% }! t, ?0 a" C4 e
7 - 1 - The Problem of Overfitting (10 min).mkv. G3 n/ N  E' g8 {+ g+ u) f2 z
7 - 2 - Cost Function (10 min).mkv
7 - 3 - Regularized Linear Regression (11 min).mkv- v% e4 k, {' m8 ^2 |4 w
7 - 4 - Regularized Logistic Regression (9 min).mkv" r9 G  G4 c$ S2 q0 A+ z* I4 j* u1 a! D1 Z
8 - 1 - Non-linear Hypotheses (10 min).mkv' F) L. ~0 g6 G+ s! e
8 - 2 - Neurons and the Brain (8 min).mkv
8 - 3 - Model Representation I (12 min).mkv% x$ V' Y( E: W2 a
8 - 4 - Model Representation II (12 min).mkv/ V0 D/ I4 N- e$ M2 K+ c" O# H
8 - 5 - Examples and Intuitions I (7 min).mkv
8 - 6 - Examples and Intuitions II (10 min).mkv% q# B' [) l  ^' S& }7 f& o
8 - 7 - Multiclass Classification (4 min).mkv5 A. `' N( x* ^& Z' u+ a# s8 t( B8 b1 G
9 - 1 - Cost Function (7 min).mkv& O  o6 Z& d4 m/ S: L6 k
9 - 2 - Backpropagation Algorithm (12 min).mkv
9 - 3 - Backpropagation Intuition (13 min).mkv: Q; v# w( _7 r8 y! ~" o( U4 U/ @' i/ M) a, H8 M& o3 l' Q
9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv4 f/ j, v  M9 a6 l' L
9 - 5 - Gradient Checking (12 min).mkv9 E8 U# \  {5 J- r* I' F4 U
9 - 6 - Random Initialization (7 min).mkv' M7 y" l. z  u4 l8 S6 b, T+ `( ^5 u
9 - 7 - Putting It Together (14 min).mkv. Y% L/ b# q2 m  W! O, ^
9 - 8 - Autonomous Driving (7 min).mkv' Y' q& T: M/ V3 E( |8 @1 c. a, y* g& p3 H
10 - 1 - Deciding What to Try Next (6 min).mkv8 Q+ M% Y5 s7 s( b5 l. D- m' e1 q8 [7 v0 X! j$ P/ r+ f4 [. P
10 - 2 - Evaluating a Hypothesis (8 min).mkv
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv# R7 A9 j1 ?, H! Y
10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv9 N! ?" a! |: W/ M, v" i) G
10 - 5 - Regularization and Bias_Variance (11 min).mkv+ W% O- {. s- c# d0 h9 p# I
10 - 6 - Learning Curves (12 min).mkv- b8 Q) Y% p& P' z0 M- F, z+ J0 g1 c7 _6 l5 c
10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
11 - 1 - Prioritizing What to Work On (10 min).mkv! h8 s8 ?# j( J# P, {3 Q/ i0 D' m
11 - 2 - Error Analysis (13 min).mkv% ?5 l8 [2 _; H0 b  z9 C& v
11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
11 - 4 - Trading Off Precision and Recall (14 min).mkv  ~3 K% b* l: t# ?
11 - 5 - Data For Machine Learning (11 min).mkv3 X5 x( I% b% g- i2 z& e7 a
12 - 1 - Optimization Objective (15 min).mkv) D) v: I$ q. z5 @7 c
12 - 2 - Large Margin Intuition (11 min).mkv! e# R: q6 W* m, F5 P! W
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv2 I) ]3 n! @4 y! l2 ], u
12 - 4 - Kernels I (16 min).mkv* r" Z8 Y7 b5 ]5 U) k
12 - 5 - Kernels II (16 min).mkv! P' p6 ?, S/ m1 ^$ ^; G. P- M) A0 H3 Z6 O/ |% R+ r
12 - 6 - Using An SVM (21 min).mkv( v! Q6 j7 D. Q3 ]$ f0 |5 \; j1 s& K: k7 v' a; L; J
13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv1 U) c" {( [! O4 p. |0 W4 d7 n9 H5 r: c! u; x) ?' m1 a: G
13 - 2 - K-Means Algorithm (13 min).mkv+ ^, ~5 t/ L( l3 J6 k/ E3 i1 d! J3 r0 c2 `
13 - 3 - Optimization Objective (7 min)(1).mkv: J, S/ Y- C" F% b
13 - 3 - Optimization Objective (7 min).mkv
13 - 4 - Random Initialization (8 min).mkv5 M* Z+ h5 d: P! f: v
13 - 5 - Choosing the Number of Clusters (8 min).mkv
14 - 1 - Motivation I_ Data Compression (10 min).mkv+ @& F$ r2 X2 @+ l6 Y/ w0 b; m- B* Q
14 - 2 - Motivation II_ Visualization (6 min).mkv3 q) M6 ^* k' g' L+ h
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv1 A4 n1 u+ G1 c; P# g% e! H3 p
14 - 4 - Principal Component Analysis Algorithm (15 min).mkv7 D. y2 C# h( ^' h) I3 [
14 - 5 - Choosing the Number of Principal Components (11 min).mkv( ?4 x" i$ [0 `- g, w  e' E  g5 ]- L& F( ~& C
14 - 6 - Reconstruction from Compressed Representation (4 min).mkv% k) c) s0 O) ]5 }2 s' @0 s9 F0 v+ b+ ^/ e0 J! n1 A1 G
14 - 7 - Advice for Applying PCA (13 min).mkv* n+ u5 s$ l5 K, Q3 W& b7 h7 _5 V: b% q  U
15 - 1 - Problem Motivation (8 min).mkv7 p, f1 ~! j. m7 t) C7 ^
15 - 2 - Gaussian Distribution (10 min).mkv
15 - 3 - Algorithm (12 min).mkv5 u# w' x0 D; A
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv2 f& J$ d, I- Y" w6 A
15 - 6 - Choosing What Features to Use (12 min).mkv1 ^4 J. z5 ?; n$ Q
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv) w. Z6 u& d5 w( z# U+ d, W3 g/ u+ c; p+ c2 l
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv' g3 B# i# T) i# e9 Q" w& _) X- l  \5 T$ d% G
16 - 1 - Problem Formulation (8 min).mkv7 g1 X' n) W. X" L/ ?
16 - 2 - Content Based Recommendations (15 min).mkv
16 - 3 - Collaborative Filtering (10 min).mkv% Y  o, N  m3 k: _* v- O) n/ Q5 e
16 - 4 - Collaborative Filtering Algorithm (9 min).mkv6 x0 J' b* t% D' u3 W, ]
16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv1 @# g5 E& d% y2 ~9 a
17 - 1 - Learning With Large Datasets (6 min).mkv) z' e) c. p) C" Z6 {  B  j" R
17 - 2 - Stochastic Gradient Descent (13 min).mkv- A+ x0 B! I3 S% |3 z8 `
17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv) W: q( g  a# e* q$ z- S$ f
17 - 5 - Online Learning (13 min).mkv! B2 V9 v& E  ~2 O, a; g* ?4 ?5 M; B% B. J/ r
17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
18 - 1 - Problem Description and Pipeline (7 min).mkv' N. w+ x5 o7 v3 d
18 - 2 - Sliding Windows (15 min).mkv! ?- o3 i! T& W1 x2 d- E7 w. ?7 d3 r4 e
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv! R+ _; r( M8 j, B3 L( N5 u* u3 P+ P1 L
18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
19 - 1 - Summary and Thank You (5 min).mkv/ [! k7 z3 m# Q3 M: r* v
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