A MATLAB implementation of the TensorFlow Neural Network Playground

Inspired by the TensorFlow Neural Networks Playground interface readily available online at http://playground.tensorflow.org/, this is a MATLAB implementation of the same Neural Network interface for using Artificial Neural Networks for regression and classification of highly non-linear data. The interface uses the HG1 graphics system in order to be compatible with older versions of MATLAB. A secondary purpose of this project is to write a vectorized implementation of training Artificial Neural Networks with Stochastic Gradient Descent as a means of education and to demonstrate the power of MATLAB and matrices. The goal for this framework is given randomly generated training and test data that fall into two classes that conform to certain shapes or specifications, and given the configuration of a neural network, the goal is to perform either regression or binary classification of this data and interactively show the results to the user, specifically a classification or regression map of the data, as well as numerical performance measures such as the training and test loss and their values plotted on a performance curve over each iteration. The architecture of the neural network is highly configurable so the results for each change in the architecture can be seen immediately.
There are two files that accompany this project:

1. NeuralNetApp.m: The GUI that creates the interface as seen on TensorFlow Neural Networks Playground but is done completely with MATLAB GUI elements and widgets.

2. NeuralNet2.m: The class that performs the Neural Network training via Stochastic Gradient Descent. This is used in NeuralNetApp.m