Neural Network from Scratch

To delve deeper into the intricacies of neural networks, I am currently constructing one entirely from the ground up using Python. As of now, I have implemented the capability to create neural networks of various sizes, with adjustable parameters such as the number of layers and neurons in each layer. I have also incorporated different activation functions, like softmax and ReLU, and a loss calculation mechanism. At this stage, the network learns through random adjustments to weights and biases, but I plan to integrate a linear regression algorithm in the near future

This project is based on the amazing book Neural Networks from Scratch in Python by Harrison Kinsley and Daniel KukieĊ‚a


Neural Network Training Example

An example of a neural network learning to classify various data points within a simple dataset. After a predefined number of iterations, the neural network displays the set of weights and biases that achieved the highest performance, as well as the best-achieved loss and accuracy