This repository is a personal project designed to delve into the inner workings of machine learning (ML) and deep learning (DL) algorithms. By implementing these algorithms from scratch, the focus is on understanding their mathematical foundations, step-by-step derivations, and practical applications. This hands-on approach aims to bridge the gap between theoretical concepts and their real-world implementations.
The primary objective of this project is to enhance my comprehension of the core principles and mechanics underlying ML and DL. By constructing these algorithms from the ground up, I aim to solidify my mathematical understanding, improve my coding skills, and develop a deeper appreciation for the intricacies of these technologies.
This repository includes the following topics:
- Linear Regression: Mathematical derivation and implementation for predictive modeling.
- Logistic Regression: Understanding binary classification through gradient descent.
- Neural Networks: Step-by-step construction and training of simple neural networks.
- Optimization Algorithms: Exploration of gradient descent, momentum, and Adam optimizers.
- Matrix Operations: Fundamental linear algebra concepts essential for ML/DL.
- Loss Functions: Analysis of common loss functions and their roles in optimization.
This project is intended solely for personal learning and exploration. The implementations prioritize clarity and educational value over computational efficiency or scalability. It is not designed for production use or deployment in real-world systems.
You are welcome to explore the code, mathematical explanations, and derivations provided in this repository. While contributions and feedback are appreciated, the primary intent is to serve as a self-contained learning resource. Feel free to adapt the content for your own educational purposes.