A practical portfolio demonstrating the mathematical foundations of Artificial Intelligence and Machine Learning, developed for an AI & Algorithms course.
This repository contains essential concepts implemented from scratch using Python and Jupyter Notebooks, focusing on the core mechanics that drive neural networks.
| Concept | Focus | Notebook |
|---|---|---|
| Vectors & Linear Algebra | Data representation, vector operations, and their role in machine learning. | Vectors.ipynb |
| Gradients & Calculus | The calculation of gradients and the mechanism behind Gradient Descent for model optimization. | Gradients.ipynb |
| Activation Functions | Implementation of key non-linear activation functions (e.g., ReLU, Sigmoid) used in neural network layers. | Activation functions.ipynb |
- Language: Python
- Environment: Jupyter Notebook for easy reproducibility.
- Libraries: Primarily uses NumPy for numerical computation.