ml-theory-python

This repository summarize the basic algorithm and theory in machine learning area. And there are some implementation code(mathematics, statistics, etc..) using Python. The reason for the existence of each chapter is for study only.

Table of Contents

• 1. Statistics
  • probability
  • distribution
  • estimation
  • testing
  • bayesian statistics
  • entropy
  • time series
• 2. Regression
  • linear regression
  • logistic regression
  • optimizer
  • regularization
• 3. Linear Algebra
  • background knowledge
  • dimensionality reduction
  • matrix factorization
• 4. Neural Network
  • Pytorch Examples
    • basic
    • regression
    • logistic regression
    • fnn
  • pending..
• 5. Recommender
  • Factorization Machine
  • Factorization Machine vector analysis
  • Wide and Deep
  • pending..
• 6. MAB
  • E-greedy
  • Thompson Sampling
  • pending..

Getting Started

$ git clone https://github.com/yoonkt200/ml-theory-python.git
$ set python path to `venv/` folder
$ run ml-theory-python/{chapter-name}/{algorithm-name.py or .ipynb}/

Dependencies

• Anaconda 4.7.10+
• Python 3.7.7+

Information

• Writer : Yoon Ki Tae / @github / @blog
• Created : 2018. 05