Photo Credit: Liam Kay
AI Learning Hub is an open-sourced machine learning handbook. We contribute to this repo by summarizing interesting blog, course and/or notes of machine learning, deep learning, computer vision, robotics and/or statistics. We also intend to provide each post with Chinese version.
We do this because we love AI and sharing. Excellent materials are the step stone for learning AI. We think everyone is deserved a chance to study AI with excellent materials. We welcome anyone to join us to make it better!
And you own whatever you write here!
We are looking for any related notes that are genuinely created by your own. By genuinity, we mean one of the following:
You create and write the contents of notes from scratch. Everything is original.
You summarize contents from related course(s), book(s) and note(s). You can merge contents from multiple sources. Although this is expected to be a summary, your summary should be original.
You translate one of the notes in THIS repo.
We provide with two ways to view and learn the blogs.
The best way to view the contents of any blog is to view the homepage of the author of that blog that especially interests you. The information of author’s homepage of each blog is listed in this README and will be updated as any changes happen.
We highly recommend this way to view the contents of any blog.
Install Ruby environment. Instructions can be found here.
gem install jekyll bundler
git clone https://github.com/Wei2624/AI_Learning_Hub.git cd AI_Learning_Hub bundle install bundle exec jekyll build
_sitedirectory, you can find
.htmlfile. Then, you are able to view them locally.
You are very welcome to join us to improve this repo more!
The easiest way to contribute is to fork this project and write your own contents. Remember that you own whatever you write.
To unify the style of each blog, you should use
markdown as the syntax with
mathjax as a plugin for math. Of course, you can insert
html code whenever you want. An example of header of a blog can be as below:
--- layout: single mathjax: true title: Regularization and Model Selection share: true permalink: /MachineLearning/sv_regularization_model_selection/ ---
layout, you better either choose
single where comments are enabled or
archive where comments are disabled. For more layout options, you can view here.
permalink is a slef-defined relative url path. If you want to host up your blog, you can append
permalink to your
You better follow this procedure so that people can run
ruby command to generate local page for view.
You can put up your own blog. The easiest way to do this is to use submodule from git.
Essentially, you have your own repo. Then you can run
git submodule command to add this repo as a subdirectory to your original repo. This repo will just become one of the folders in your repo. You can access whatever you write here.
Distribution of contents without author’s permission is strictly prohibited.
Please respect the authorship of each blog there. If you want to distribute them, you can ask the author for permission. Every author here has all the rights to their written blog and is fully responsible for their written blogs.
|ML||Generative Algorithm||EN||Wei Zhangemail@example.com|
|ML||Discriminative Algorithm||EN||Wei Zhangfirstname.lastname@example.org|
|ML||Support Vector Machine||EN||Wei Zhangemail@example.com|
|ML||Bias-Varaince and Error Analysis||EN||Wei Zhangfirstname.lastname@example.org|
|ML||Learning Theory||EN||Wei Zhangemail@example.com|
|ML||Regularization and Model Selection||EN||Wei Zhangfirstname.lastname@example.org|
|ML||Online Learning and Perceptron Algorithm||EN||Wei Zhangemail@example.com|
|ML||EM Algorithm||EN||Wei Zhangfirstname.lastname@example.org|
|ML||Variational Inference||EN||Wei Zhangemail@example.com|
|DL||Nerual Networks||EN||Wei Zhangfirstname.lastname@example.org|
|ML||Generative Algorithm||CH||Zishi Yan||WeChat:air-sowhat|
|ML||Discriminative Algorithm||CH||Xiaoxiao Lei||WeChat: Dark417|
|ML||Support Vector Machine||CH||Zishi Yan||WeChat:air-sowhat|
|ML||Bias-Varaince and Error Analysis||CH||Xiaoxiao Lei||WeChat: Dark417|
|ML||Regularization and Model Selection||CH||Xiaoxiao Lei||WeChat: Dark417|