Welcome to the "linear-algebra-optimization-ml" repository! This repository is designed to provide a comprehensive introduction to the essential mathematical concepts that underlie many advanced machine learning techniques.
The topics covered in this repository include:
- Dot products and hyperplanes
- Halfspaces and distance
- Loss minimization in classification
- The need for calculus in ML
- Towards gradient descent
- Gradient descent in action
- Constrained optimization
- Principal component analysis
By understanding these concepts, you'll be able to build a strong mathematical foundation for advanced machine learning techniques.
The repository includes lecture notes and associated code to help you practice and reinforce your understanding of these concepts. I recommend following the topics in order, as they build on each other and provide a comprehensive introduction to the subject.
I welcome contributions to this repository! If you find an error or have a suggestion for improvement, please create a pull request with your changes.
I hope you find this repository helpful and informative, and I look forward to helping you build a strong mathematical foundation for advanced machine learning techniques!