This project is part of the Udacity Deep Learning nanodegree in which I successfully attained a scholarship. This first project is rather basic and it is meant to better grasp how forward pass, gradient descent and backpropagation efectively work. It is more focused on the underlying math of it all. It is implemented using PyTorch.
The project is meant to build a neural network from scratch and apply it to a prediction problem on a bike-sharing dataset. The data stems from UCI Machine Learning Database (Porto 😄)
