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

• [x] 01 Introduction 1~26
• [x] 02 Probability 27~64 (练习略)
• [x] 03 Generative models for discrete data 65~96(练习略)
• [x] 04 Gaussian models 97~148(练习略)
• [x] 05 Bayesian statistics 149~190(练习略)
• [x] 06 Frequentist statistics 191~216(练习略)
• [x] 07 Linear regression 217~244(练习略)
• [x] 08 Logistic regression 245~280(练习略)
• [x] 09 Generalized linear models and the exponential family 281~306(练习略)
• [x] 10 Directed graphical models (Bayes nets) 307~336(练习略)
• [x] 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

• [x] 01 Introduction 1~26
• [x] 02 Probability 27~64 (Exercise slightly)
• [x] 03 Generative models for discrete data 65~96 (execution slightly)
• [x] 04 Gaussian models 97~148 (execution slightly)
• [x] 05 Bayesian statistics 149~190 (practice slightly)
• [x] 06 Frequentist statistics 191~216 (execution slightly)
• [x] 07 Linear regression 217~244 (practice slightly)
• [x] 08 Logistic regression 245~280 (practice slightly)
• [x] 09 Generalized linear models and the exponential family 281~306 (execution slightly)
• [x] 10 Directed graphical models (Bayes nets) 307~336 (practice slightly)
• [x] 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