This project is a basic implementation of a neural network based primarily on the code and ideas presented in the online book Neural Networks and Deep Learning by Michael Nielsen.

The primary goals of this project are:

• To understand and internalize the basic concepts behind neural networks, the backpropagation alogrithm, and deep learning.
• To create an object-oriented implementation of a neural network with an intuitive API in a language that is less commonly used for deep learning.

This project targets .Net Core version 2.1. In order to build and run the project you'll need to download and install the SDK from Microsoft

The project currently outputs an executable where the network structure, training data, and test data are all explicitly defined in the file Network/Program.cs.

The current configuration builds a network with a single hidden layer of 30 neurons. The network is trained and tested with the training and testing images from the MNIST dataset.

In order to use this file as-is, you will need to download the 4 data files (images and labels for both training and test sets) which you can get from The MNIST Database. Unzip the files to Network/data/.

$ cd Network
$ dotnet build
$ dotnet run