mulintia / mlapp-cn

A Chinese Notes of MLAPP，MLAPP 中文笔记项目 https://zhuanlan.zhihu.com/python-kivy

MLAPP-CN

MLAPP 中文笔记项目 https://zhuanlan.zhihu.com/python-kivy

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 https://zhuanlan.zhihu.com/python-kivy

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