$ Rust Neural Network
This project implements a basic feedforward neural network in Rust. It's designed to be a clear and understandable example of how neural networks work, incorporating fundamental concepts like activation functions and backpropagation for training.
### features
- -Activation Functions - Supports ReLU, Sigmoid, Tanh, and Linear activation functions.
- -Feedforward Propagation - Calculates the output of the network given an input.
- -Backpropagation Algorithm - Implements backpropagation for training the network.
- -Weight Initialization - Uses He or Xavier scaling for initializing weights.
- -Serialization - Network structure can be serialized and deserialized using `serde`.
### example usage
The `main.rs` example demonstrates how to create, train, and use the neural network for a simple task.
It's a foundational project for anyone looking to understand the mechanics of neural networks from a low-level perspective using Rust.