This repository contains self-implemented machine learning experiments and mathematical explorations. The primary goal is to understand core concepts from first principles by deriving equations, implementing algorithms from scratch, and analyzing optimization behavior through controlled experiments.
This repository functions as a structured lab notebook for experimentation, comparison, and conceptual refinement.
- Implement algorithms without relying on high-level ML libraries
- Translate mathematical formulations into working code
- Experiment with optimization methods and analyze convergence behavior
- Document observations and insights systematically
- Build deeper intuition about learning dynamics
Machine-Learning-Lab/
├── experiments/
│ ├── linear-regression/
│ ├── logistic-regression/
│ └── neural-networks/
├── notes/
│ ├── mathematical-derivations/
│ └── analysis/
├── requirements.txt
└── README.md
Each experiment folder contains:
- Implementation
- Mathematical formulation
- Observations and analysis
The structure may evolve as new topics and experiments are added.
- Python
- NumPy
- Matplotlib
- Jupyter Notebook
This repository emphasizes clarity, reproducibility, and conceptual depth. The focus is on understanding why algorithms work, not just how to use them.
pip install -r requirements.txtMIT