LearningNotes: A repository from lelouchsola

Greeting, this is Lelouchsola's learning road map!

I use this repository to store my notes for books. Note Academic Papers are not included in this road map.

Let's start my learning road map!

I built my own learning road map by referring to the AI-roadmap:
https://github.com/apachecn/ai-roadmap/tree/master/ai-union-201904

Part I: Mathmatics

1. optimization:

a.《Convex Optimization: Algorithms and Complexity》
b.《Optimization Methods for Large-Scale Machine Learning》
c.《Convex Optimization》by Boyd 
d.《不确定规划及应用》 by 刘宝碇 
e.《Introduction to Operations Research》by S. Hillier 中文《运筹学导论》
f. DrSalimian 的线性规划系列视频 
    https://www.youtube.com/playlist?list=PLUm0dA6802wao5iSrkhnMDSLGu5bg_FtM 
g. "Non-convex Optimization for Machine Learning" by Prateek Jain. 
h. lectures on stochastic programming, by A Shapiro
i. Robust Optimization, by Aharon Ben-Tal

Note: The most important thing is how to use mathmatic to describe the problem and build up the corresponding model, but not how to prove those theroms. It is better to learn optimization by using softwares to handle the practical problems.

2. Linear algebra:

MIT 18.06
矩阵求导术: 
    https://zhuanlan.zhihu.com/p/24709748
    https://zhuanlan.zhihu.com/p/24863977

3. Statistics:

《An Introduction to Statistical Learning》 https://github.com/JWarmenhoven/ISLR-python

Part II: Computer science

a. CS61B by UCB
b. 《算法图解》  https://github.com/zhanwen/AlgorithmDiagram

Part III: Machine Learning

a. 《机器学习》 周志华  
b. 《统计学习方法》 李航  
c. 《Scikit-Learn 与 TensorFlow 机器学习实用指南》 https://github.com/apachecn/hands-on-ml-zh  
d. 《Machine Learning in Action》  
e.  Kaggle https://github.com/apachecn/Interview

Part IV: Deep Learning

a. 《Deep Learning》 on Coursera  
    exercies https://github.com/stormstone/deeplearning.ai  
b. 《Deep Learning》 https://github.com/zsdonghao/deep-learning-book 
c.  PyTorch官方文档 https://pytorch.apachecn.org/docs/1.0/  
d. 深度学习入门 https://www.zhihu.com/question/26006703/answer/536169538  
e. CS231N or fast.ai Course  
f. 神经网络与深度学习 讲义、练习 https://nndl.github.io/  
g. 《动手学深度学习》https://zh.gluon.ai/

Part V: Reinforcement Learning

a. 《Reinforcement Learning : An introduction》  
    https://github.com/ShangtongZhang/reinforcement-learning-an-introduction  
b. 强化学习入门 https://www.zhihu.com/question/277325426/answer/411907338
c. 莫烦RL教程 https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/  
d. RL学习资料整理 https://www.zhihu.com/question/333671830/answer/745818039
e. CS294-112 (CS285) UCB http://rail.eecs.berkeley.edu/deeprlcourse/

Part VI: Latex

a. https://www.zhihu.com/question/62943097/answer/203670095

Part VII: AI applition in Power System

a."Artificial intelligence in power system optimization"

lelouchsola/LearningNotes