This is the first article in the Machine Learning Series. It covers the basics of optimization(GD,SGD,SVRG,Mirror Descent,Linear Coupling), generalization(No Free Lunch, PAC Learning, VC Dimension), and supervised learning(Linear Regression, Logistic Regression, Compressed Sensing).

This blog post is a summary of my notes on complex analysis and its application in electrostatics.

This is the first part of the Natural Language Processing Series. It covers classical methods in natural language processing, including CFG, LSA, HMM, N-gram, Word2Vec, etc.