This project involves implementing a neural network from scratch, providing a deep understanding of the underlying mechanics of neural networks without relying on high-level libraries. This hands-on approach is essential for grasping the fundamentals of deep learning.
- Data Preprocessing: Techniques to prepare data for training.
- Network Architecture: Custom implementation of neural network layers.
- Forward Propagation: Calculation of neuron activations.
- Backward Propagation: Gradient calculation for weight updates.
- Optimization: Implementation of gradient descent.
- Evaluation: Assessing the performance of the neural network.
The notebook includes visualizations and performance metrics to help you understand the network's behavior and effectiveness.