{"id":13694394,"url":"https://github.com/cycleuser/MLAPP-CN","last_synced_at":"2025-05-03T01:33:20.146Z","repository":{"id":52373176,"uuid":"132303103","full_name":"cycleuser/MLAPP-CN","owner":"cycleuser","description":"A Chinese Notes of MLAPP，MLAPP 中文笔记项目  https://zhuanlan.zhihu.com/python-kivy","archived":true,"fork":false,"pushed_at":"2021-03-08T11:45:49.000Z","size":62100,"stargazers_count":359,"open_issues_count":7,"forks_count":74,"subscribers_count":39,"default_branch":"master","last_synced_at":"2024-11-08T05:03:05.155Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cycleuser.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-05-06T03:22:24.000Z","updated_at":"2024-11-01T16:43:17.000Z","dependencies_parsed_at":"2022-09-14T14:10:39.906Z","dependency_job_id":null,"html_url":"https://github.com/cycleuser/MLAPP-CN","commit_stats":null,"previous_names":["cycleuser/mlapp-cn","kivy-cn/mlapp-cn"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycleuser%2FMLAPP-CN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycleuser%2FMLAPP-CN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycleuser%2FMLAPP-CN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cycleuser%2FMLAPP-CN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cycleuser","download_url":"https://codeload.github.com/cycleuser/MLAPP-CN/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224346574,"owners_count":17296242,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T17:01:30.851Z","updated_at":"2024-11-12T20:32:20.404Z","avatar_url":"https://github.com/cycleuser.png","language":"HTML","funding_links":[],"categories":["HTML"],"sub_categories":[],"readme":"# MLAPP-CN\r\n\r\nMLAPP 中文笔记项目 \r\n\r\n## 在线阅读\r\n\r\n\u003chttps://kivy-cn.github.io/MLAPP-CN\u003e\r\n\r\n## 笔记项目概述\r\n\r\n本系列是一个新坑, 还希望大家批评指正！\r\n\r\n### 书中疑似错误记录\r\n\r\nhttps://github.com/Kivy-CN/MLAPP-CN/blob/master/Error.md\r\n\r\n### 笔记进度追踪\r\n\r\n- [x] 01 Introduction 1~26\r\n- [x] 02 Probability 27~64 (练习略)\r\n- [x] 03 Generative models for discrete data 65~96(练习略)\r\n- [x] 04 Gaussian models 97~148(练习略)\r\n- [x] 05 Bayesian statistics 149~190(练习略)\r\n- [x] 06 Frequentist statistics 191~216(练习略)\r\n- [x] 07 Linear regression 217~244(练习略)\r\n- [x] 08 Logistic regression 245~280(练习略)\r\n- [x] 09 Generalized linear models and the exponential family 281~306(练习略)\r\n- [x] 10 Directed graphical models (Bayes nets) 307~336(练习略)\r\n- [x] 11 Mixture models and the EM algorithm 337~380(当前进度 337)\r\n- [ ] 12 Latent linear models 381~420\r\n- [ ] 13 Sparse linear models 421~478\r\n- [ ] 14 Kernels 479~514\r\n- [ ] 15 Gaussian processes 515~542\r\n- [ ] 16 Adaptive basis function models 543~588\r\n- [ ] 17 Markov and hidden Markov models 589~630\r\n- [ ] 18 State space models 631~660\r\n- [ ] 19 Undirected graphical models (Markov random fields) 661~706\r\n- [ ] 20 Exact inference for graphical models 707~730\r\n- [ ] 21 Variational inference 731~766\r\n- [ ] 22 More variational inference 767~814\r\n- [ ] 23 Monte Carlo inference 815~836\r\n- [ ] 24 Markov chain Monte Carlo (MCMC) inference 837~874\r\n- [ ] 25 Clustering 875~906\r\n- [ ] 26 Graphical model structure learning 907~944\r\n- [ ] 27 Latent variable models for discrete data 945~994\r\n- [ ] 28 Deep learning 995~1009\r\n\r\n# MLAPP-CN\r\n\r\nMLAPP Chinese Notes Project\r\n\r\n## Read Online\r\n\r\n\u003chttps://kivy-cn.github.io/MLAPP-CN\u003e\r\n\r\n## Note Project Overview\r\n\r\nThis series is a new pit, and I hope everyone will criticize me!\r\n\r\n### Suspected error record in book\r\n\r\nhttps://github.com/Kivy-CN/MLAPP-CN/blob/master/Error.md\r\n\r\n### note progress tracking\r\n\r\n- [x] 01 Introduction 1~26\r\n- [x] 02 Probability 27~64 (Exercise slightly)\r\n- [x] 03 Generative models for discrete data 65~96 (execution slightly)\r\n- [x] 04 Gaussian models 97~148 (execution slightly)\r\n- [x] 05 Bayesian statistics 149~190 (practice slightly)\r\n- [x] 06 Frequentist statistics 191~216 (execution slightly)\r\n- [x] 07 Linear regression 217~244 (practice slightly)\r\n- [x] 08 Logistic regression 245~280 (practice slightly)\r\n- [x] 09 Generalized linear models and the exponential family 281~306 (execution slightly)\r\n- [x] 10 Directed graphical models (Bayes nets) 307~336 (practice slightly)\r\n- [x] 11 Mixture models and the EM algorithm 337~380 (current progress 337)\r\n- [ ] 12 Latent linear models 381~420\r\n- [ ] 13 Sparse linear models 421~478\r\n- [ ] 14 Kernels 479~514\r\n- [ ] 15 Gaussian processes 515~542\r\n- [ ] 16 Adaptive basis function models 543~588\r\n- [ ] 17 Markov and hidden Markov models 589~630\r\n- [ ] 18 State space models 631~660\r\n- [ ] 19 Undirected graphical models (Markov random fields) 661~706\r\n- [ ] 20 Exact inference for graphical models 707~730\r\n- [ ] 21 Variational inference 731~766\r\n- [ ] 22 More variational inference 767~814\r\n- [ ] 23 Monte Carlo inference 815~836\r\n- [ ] 24 Markov chain Monte Carlo (MCMC) inference 837~874\r\n- [ ] 25 Clustering 875~906\r\n- [ ] 26 Graphical model structure learning 907~944\r\n- [ ] 27 Latent variable models for discrete data 945~994\r\n- [ ] 28 Deep learning 995~1009\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcycleuser%2FMLAPP-CN","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcycleuser%2FMLAPP-CN","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcycleuser%2FMLAPP-CN/lists"}