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斯坦福大学吴恩达机器学习课程 6 ]7 w6 U: d- d
1 - 1 - Welcome (7 min).mkv
1 - 2 - What is Machine Learning_ (7 min).mkv! G3 p: n0 ^+ K, z$ W5 w' Z
1 - 3 - Supervised Learning (12 min).mkv* b8 D- R3 J5 P/ O" d. A0 i
1 - 4 - Unsupervised Learning (14 min).mkv
2 - 1 - Model Representation (8 min).mkv; ^, F" X- |0 H5 }6 ~) P
2 - 2 - Cost Function (8 min).mkv
2 - 3 - Cost Function - Intuition I (11 min).mkv
2 - 4 - Cost Function - Intuition II (9 min).mkv
2 - 5 - Gradient Descent (11 min).mkv
2 - 6 - Gradient Descent Intuition (12 min).mkv5 f6 ]0 `2 n( [/ M2 l
2 - 7 - GradientDescentForLinearRegression (6 min).mkv
2 - 8 - What_'s Next (6 min).mkv1 P& Q3 k; f+ F! D# U& ~ A
3 - 1 - Matrices and Vectors (9 min).mkv
3 - 2 - Addition and Scalar Multiplication (7 min).mkv
3 - 3 - Matrix Vector Multiplication (14 min).mkv
3 - 4 - Matrix Matrix Multiplication (11 min).mkv# ]! t& e0 }! a+ W8 N9 h/ x
3 - 5 - Matrix Multiplication Properties (9 min).mkv* ]. o* i9 R( d+ J/ i5 D2 n7 m5 _ Y
3 - 6 - Inverse and Transpose (11 min).mkv' l1 ?3 E2 r/ }0 z4 k
4 - 1 - Multiple Features (8 min).mkv6 F2 z w# G& X4 }& K G
4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv# G- q- o! f9 F4 X3 ^
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv7 I' K0 R6 I4 _+ ]! x# h1 Z
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv" V$ n$ ^, [/ L9 _; @/ M, q
4 - 5 - Features and Polynomial Regression (8 min).mkv& L5 N* ?: k2 f6 s# \% G
4 - 6 - Normal Equation (16 min).mkv
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv' c, m# K+ l, f) k
5 - 1 - Basic Operations (14 min).mkv0 ^; m: k+ ^, E$ \" n) \
5 - 2 - Moving Data Around (16 min).mkv
5 - 3 - Computing on Data (13 min).mkv& I# m2 p( k" S( y; V) ~3 b
5 - 4 - Plotting Data (10 min).mkv+ c" X3 N& _7 \: `0 U# t$ k
5 - 5 - Control Statements_ for, while, if statements (13 min).mkv5 n* N+ v( Z- Y D
5 - 6 - Vectorization (14 min).mkv6 k3 Z$ l5 J7 z2 T% K. ~
5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv3 f* N; ~, |: c- {% F
6 - 1 - Classification (8 min).mkv
6 - 2 - Hypothesis Representation (7 min).mkv
6 - 3 - Decision Boundary (15 min).mkv
6 - 4 - Cost Function (11 min).mkv" i5 n: {$ @ U& _; u
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv9 C5 h( D, X! G x& s* n
6 - 6 - Advanced Optimization (14 min).mkv" P: v1 j( A8 g
6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv
7 - 1 - The Problem of Overfitting (10 min).mkv
7 - 2 - Cost Function (10 min).mkv& W/ e8 k! L& Q" q
7 - 3 - Regularized Linear Regression (11 min).mkv7 x& n9 S# o- _5 J6 A
7 - 4 - Regularized Logistic Regression (9 min).mkv; z9 ?, j+ Z4 D/ e
8 - 1 - Non-linear Hypotheses (10 min).mkv
8 - 2 - Neurons and the Brain (8 min).mkv# h& P" E9 V2 J- g
8 - 3 - Model Representation I (12 min).mkv; `. d# O8 T4 p; ?
8 - 4 - Model Representation II (12 min).mkv$ n$ b, F( Y7 e# Y8 m( I
8 - 5 - Examples and Intuitions I (7 min).mkv Y& q1 L$ M8 N+ l" y% |
8 - 6 - Examples and Intuitions II (10 min).mkv1 X* J. A+ ~4 s( I/ S
8 - 7 - Multiclass Classification (4 min).mkv
9 - 1 - Cost Function (7 min).mkv; h$ ~1 N+ v7 D, q/ F
9 - 2 - Backpropagation Algorithm (12 min).mkv: p) r1 K" p$ h: E! v
9 - 3 - Backpropagation Intuition (13 min).mkv3 k4 r2 N! k" h1 @
9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv
9 - 5 - Gradient Checking (12 min).mkv' m8 ^, V$ n4 a2 S9 \9 @2 `
9 - 6 - Random Initialization (7 min).mkv$ n2 e$ X( k3 z6 X: c
9 - 7 - Putting It Together (14 min).mkv) y+ m- j. h+ Y, W: h
9 - 8 - Autonomous Driving (7 min).mkv
10 - 1 - Deciding What to Try Next (6 min).mkv
10 - 2 - Evaluating a Hypothesis (8 min).mkv
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv8 y# S/ ^3 n2 Q `
10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv( Z; C7 Z: \2 f+ Z2 x) ]
10 - 5 - Regularization and Bias_Variance (11 min).mkv- v+ _' H+ i" x8 T( p
10 - 6 - Learning Curves (12 min).mkv$ u0 _* \- a' e% N k- S& z- {
10 - 7 - Deciding What to Do Next Revisited (7 min).mkv; ]6 o; ?& f2 k; x
11 - 1 - Prioritizing What to Work On (10 min).mkv
11 - 2 - Error Analysis (13 min).mkv
11 - 3 - Error Metrics for Skewed Classes (12 min).mkv# d0 g2 v0 R3 X. V4 c7 Q4 G
11 - 4 - Trading Off Precision and Recall (14 min).mkv
11 - 5 - Data For Machine Learning (11 min).mkv& [7 t' h2 K1 M$ a
12 - 1 - Optimization Objective (15 min).mkv! V$ }( o4 X8 I- ]0 ?3 Z& {' M
12 - 2 - Large Margin Intuition (11 min).mkv ]0 h, l8 [) R2 t* W- y
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv e$ v' F( V% G% m7 F
12 - 4 - Kernels I (16 min).mkv: I+ ~; _3 c. ~1 G2 l$ @/ e, H
12 - 5 - Kernels II (16 min).mkv
12 - 6 - Using An SVM (21 min).mkv9 |5 h+ a) Q* j- d) y8 Z3 m
13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv
13 - 2 - K-Means Algorithm (13 min).mkv
13 - 3 - Optimization Objective (7 min)(1).mkv
13 - 3 - Optimization Objective (7 min).mkv8 L6 d; ]' a1 s- A
13 - 4 - Random Initialization (8 min).mkv
13 - 5 - Choosing the Number of Clusters (8 min).mkv: X0 e( G- K. a9 r) T
14 - 1 - Motivation I_ Data Compression (10 min).mkv
14 - 2 - Motivation II_ Visualization (6 min).mkv
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv& a6 b7 Y4 D e6 ^
14 - 4 - Principal Component Analysis Algorithm (15 min).mkv
14 - 5 - Choosing the Number of Principal Components (11 min).mkv1 X. f' @; F) E8 E3 W" Y) F9 d- i5 L
14 - 6 - Reconstruction from Compressed Representation (4 min).mkv: _* z+ O% k4 ~8 {- b* R, b
14 - 7 - Advice for Applying PCA (13 min).mkv) ?* p4 B. R$ L9 n$ p
15 - 1 - Problem Motivation (8 min).mkv u- W K! [: g/ E. \. ]: f$ ~
15 - 2 - Gaussian Distribution (10 min).mkv
15 - 3 - Algorithm (12 min).mkv, V, V/ c; O3 X/ @0 Z K
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv {/ _9 O5 K3 f" G* e
15 - 6 - Choosing What Features to Use (12 min).mkv
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv
16 - 1 - Problem Formulation (8 min).mkv
16 - 2 - Content Based Recommendations (15 min).mkv! o; I. s% K d6 k0 ?
16 - 3 - Collaborative Filtering (10 min).mkv
16 - 4 - Collaborative Filtering Algorithm (9 min).mkv
16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv3 F) {+ [, B- b" m# M
16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv) @- _ v6 Z! p/ i t& \
17 - 1 - Learning With Large Datasets (6 min).mkv. j; ?( Y' ~2 i
17 - 2 - Stochastic Gradient Descent (13 min).mkv
17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv
17 - 5 - Online Learning (13 min).mkv& w3 `" |& |$ G+ r" Y$ @* S
17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
18 - 1 - Problem Description and Pipeline (7 min).mkv
18 - 2 - Sliding Windows (15 min).mkv
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv
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
pdf
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中英文字幕.rar
如何添加中文字幕.docx2 l" U) ?7 z' b
教程和笔记
机器学习课程源代码
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