MLAPP-CN
MLAPP 中文笔记项目
在线阅读
https://kivy-cn.github.io/MLAPP-CN
笔记项目概述
本系列是一个新坑, 还希望大家批评指正！
书中疑似错误记录
https://github.com/Kivy-CN/MLAPP-CN/blob/master/Error.md
笔记进度追踪
- 01 Introduction 1~26
- 02 Probability 27~64 (练习略)
- 03 Generative models for discrete data 65~96(练习略)
- 04 Gaussian models 97~148(练习略)
- 05 Bayesian statistics 149~190(练习略)
- 06 Frequentist statistics 191~216(练习略)
- 07 Linear regression 217~244(练习略)
- 08 Logistic regression 245~280(练习略)
- 09 Generalized linear models and the exponential family 281~306(练习略)
- 10 Directed graphical models (Bayes nets) 307~336(练习略)
- 11 Mixture models and the EM algorithm 337~380(当前进度 337)
- 12 Latent linear models 381~420
- 13 Sparse linear models 421~478
- 14 Kernels 479~514
- 15 Gaussian processes 515~542
- 16 Adaptive basis function models 543~588
- 17 Markov and hidden Markov models 589~630
- 18 State space models 631~660
- 19 Undirected graphical models (Markov random fields) 661~706
- 20 Exact inference for graphical models 707~730
- 21 Variational inference 731~766
- 22 More variational inference 767~814
- 23 Monte Carlo inference 815~836
- 24 Markov chain Monte Carlo (MCMC) inference 837~874
- 25 Clustering 875~906
- 26 Graphical model structure learning 907~944
- 27 Latent variable models for discrete data 945~994
- 28 Deep learning 995~1009
MLAPP-CN
MLAPP Chinese Notes Project
Read Online
https://kivy-cn.github.io/MLAPP-CN
Note Project Overview
This series is a new pit, and I hope everyone will criticize me!
Suspected error record in book
https://github.com/Kivy-CN/MLAPP-CN/blob/master/Error.md
note progress tracking
- 01 Introduction 1~26
- 02 Probability 27~64 (Exercise slightly)
- 03 Generative models for discrete data 65~96 (execution slightly)
- 04 Gaussian models 97~148 (execution slightly)
- 05 Bayesian statistics 149~190 (practice slightly)
- 06 Frequentist statistics 191~216 (execution slightly)
- 07 Linear regression 217~244 (practice slightly)
- 08 Logistic regression 245~280 (practice slightly)
- 09 Generalized linear models and the exponential family 281~306 (execution slightly)
- 10 Directed graphical models (Bayes nets) 307~336 (practice slightly)
- 11 Mixture models and the EM algorithm 337~380 (current progress 337)
- 12 Latent linear models 381~420
- 13 Sparse linear models 421~478
- 14 Kernels 479~514
- 15 Gaussian processes 515~542
- 16 Adaptive basis function models 543~588
- 17 Markov and hidden Markov models 589~630
- 18 State space models 631~660
- 19 Undirected graphical models (Markov random fields) 661~706
- 20 Exact inference for graphical models 707~730
- 21 Variational inference 731~766
- 22 More variational inference 767~814
- 23 Monte Carlo inference 815~836
- 24 Markov chain Monte Carlo (MCMC) inference 837~874
- 25 Clustering 875~906
- 26 Graphical model structure learning 907~944
- 27 Latent variable models for discrete data 945~994
- 28 Deep learning 995~1009