Neural Network from Scratch

This project implements a neural network entirely from scratch using JavaScript, with the goal of understanding the mathematics behind neural networks, particularly backpropagation.

Key Learning Points

1. Feedforward Propagation: Implemented the forward pass, understanding data flow through layers.
2. Backpropagation: Developed a thorough understanding of the algorithm, including error calculation and gradient computation.
3. Gradient Descent: Implemented stochastic gradient descent for parameter optimization.
4. Activation Functions: Explored different functions and their impact on learning.
5. Matrix Operations: Utilized extensively, reinforcing linear algebra understanding. Used some concepts from my MATH 214 class!

Implementation Details

• Network class initializes the neural network
• Methods for feedforward propagation and backpropagation
• Stochastic Gradient Descent (SGD) for training
• Helper functions for mathematical operations