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Machine Learning Codex

A comprehensive guide to machine learning and deep learning, from mathematical foundations to production implementations.

Structure​

Each topic follows a 3-page format:

  1. Overview - Intuitive concepts with rigorous mathematical proofs (expandable)
  2. Problems - Exam-style questions with detailed solutions
  3. Implementation - Code from scratch (vanilla Python, NumPy, scikit-learn/PyTorch)

Table of Contents (WIP)​

1. Mathematical Foundations​

2. Traditional Machine Learning​

3. Deep Learning​