The neural network is implemented in the BasicFeedForwardNeuralNetwork class inherited from my SimpleML project. The network trains using gradient descent with backpropagation, and implements a simple learning rate for regularization. It also provides methods InitialiseWeights(), to set the weights of the network to small random values, and Predict() to find a result based on a specified set of input values and the current weights.
BackPropagationVisualization is run/hosted within an ASP.NET Web API application, which provides a clear separation between the business logic and data (implemented in C# and served up via REST methods), and presentation and model/view/whatever (handled by AngularJS). It's not a true web application, as the data layer holds only a single neural network which maintains state (i.e. the weights and activation values). Multiple users of the UI would be accessing the same instance of the BasicFeedForwardNeuralNetwork class and overwrite each others training data. AngularJS was chosen to provide a functional and slick interface for a single user, but not to provide the multi-user capability of a true web application.
AngularJS is used to provide a single page web application interface to BackPropagationVisualization. The UI code follows the general design directions set out in the AngularJS documentation, i.e...
(coming soon...)