A simple Neural Network. It was project made as a part of the Supervised Learning course.
The project consists of 3 main parts
A class that holds the neuron's data such as weights, output, and activation function (Sigmoid).
A function to create a network of neurons. You can specify the input features count and number of neurons in each layer.
Training was achieved by basic forward and backward propagation using the Least Square Error as a loss function.