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【斯坦福大学】CS229 机器学习 · 2018年（完结·中英字幕·机翻）

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斯坦福大学

计算机科学

鬼谷良师发消息

汇集世界一流学府教育资源

Lecture 1 - Welcome Stanford CS229 Machine Learning (Autumn 2018)

01:15:20

Lecture 2 - Linear Regression and Gradient Descent Stanford CS229 Machine Learn

01:18:17

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01:32:11

Lecture 3 - Locally Weighted Logistic Regression Stanford CS229 Machine Learni

01:19:35

Lecture 4 - Perceptron Generalized Linear Model Stanford CS229 Machine Learnin

01:22:02

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57:39

Lecture 5 - GDA Naive Bayes Stanford CS229 Machine Learning (Autumn 2018)

01:18:52

Lecture 6 - Support Vector Machines Stanford CS229 Machine Learning (Autumn 201

01:20:57

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01:29:01

Lecture 7 - Kernels Stanford CS229 Machine Learning (Autumn 2018)

01:20:25

Lecture 8 - Data Splits, Models & Cross-Validation Stanford CS229 Machine Learn

01:23:26

Lecture 9 - ApproxEstimation Error ERM Stanford CS229 Machine Learning (Autumn

01:26:03

Lecture 10 - Decision Trees and Ensemble Methods Stanford CS229 Machine Learnin

01:20:41

Lecture 11 - Introduction to Neural Networks Stanford CS229 Machine Learning (A

01:20:14

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01:28:59

Lecture 12 - Backprop Improving Neural Networks Stanford CS229 Machine Learnin

01:16:38

Lecture 13 - Debugging ML Models and Error Analysis Stanford CS229 Machine Lear

01:18:55

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01:29:00

Lecture 14 - Expectation-Maximization Algorithms Stanford CS229 Machine Learnin

01:20:32

Lecture 15 - EM Algorithm Factor Analysis Stanford CS229 Machine Learning (Aut

01:19:48

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01:29:01

Lecture 16 - Independent Component Analysis RL Stanford CS229 Machine Learning

01:18:10

Lecture 17 - MDPs ValuePolicy Iteration Stanford CS229 Machine Learning (Autum

01:19:15

Lecture 18 - Continous State MDP Model Simulation Stanford CS229 Machine Learn

01:20:15

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01:29:00

Lecture 19 - Reward Model Linear Dynamical System Stanford CS229 Machine Learn

01:21:07

Lecture 20 - RL Debugging and Diagnostics Stanford CS229 Machine Learning (Autu

01:12:43

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01:29:01

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