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https://github.com/cycleuser/MLAPP-CN
A Chinese Notes of MLAPP，MLAPP 中文笔记项目 https://zhuanlan.zhihu.com/python-kivy
https://github.com/cycleuser/MLAPP-CN
Last synced: 2 months ago
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A Chinese Notes of MLAPP，MLAPP 中文笔记项目 https://zhuanlan.zhihu.com/python-kivy
- Host: GitHub
- URL: https://github.com/cycleuser/MLAPP-CN
- Owner: cycleuser
- License: gpl-3.0
- Archived: true
- Created: 2018-05-06T03:22:24.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-03-08T11:45:49.000Z (almost 4 years ago)
- Last Synced: 2024-11-08T05:03:05.155Z (2 months ago)
- Language: HTML
- Size: 59.2 MB
- Stars: 359
- Watchers: 39
- Forks: 74
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MLAPP-CN
MLAPP 中文笔记项目
## 在线阅读
## 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
## 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