Neural Network Implementation from Scratch

Deep Learning · Python

Personal Project

GitHub Repository

Built a comprehensive neural network implementation from scratch using pure NumPy, demonstrating deep understanding of neural network fundamentals. Features include flexible architecture with configurable hidden layers, advanced training with mini-batch gradient descent, comprehensive evaluation metrics, model persistence, and robustness testing. The implementation includes multiple experiments comparing different architectures and learning rates, along with detailed visualizations of training dynamics. Achieved ~97% accuracy on MNIST dataset with complete documentation and educational examples.