Feedforward Neural Network

This project is a C# implementation of a Feedforward Neural Network, a type of artificial neural network where connections between the nodes do not form a cycle. Feedforward networks are the simplest form of artificial neural networks and are commonly used for supervised learning tasks such as classification and regression.

The network structure consists of an input layer, one or more hidden layers, and an output layer. Each neuron in a layer is fully connected to neurons in the subsequent layer, and the network makes predictions by propagating inputs forward through the layers.

This implementation includes customizable parameters such as the number of hidden layers, neurons per layer, and activation functions, making it adaptable for a range of tasks. It serves as a foundational tool for experimentation with basic neural networks and machine learning algorithms.

Github link coming soon!