A Chinese Notes of MLAPP，MLAPP 中文笔记项目 https:--z-MLAPP-CN.zip

# 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 /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 /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 免责声明： 1.本资源仅供学习和交流使用，不保证其准确性、完整性、及时性或适用性。 2.本资源仅包含一般信息，不构成专业建议。在使用本资源时，请务必自行研究并谨慎决策。 3.我已尽力确保本资源的正确性和合法性，但不对其准确性、完整性和及时性做出保证。 4.本资源不应用于商业用途。 5.在使用本资源的过程中，用户应自行承担所有风险和责任，并遵守相关法律法规。 6.对于因使用本资源而产生的任何损失或损害，我概不负责。 请确保在使用本资源时仔细阅读并遵守以上免责声明。如果您有任何疑问或需要进一步帮助，请联系我。