A fully connected neural network with many options for customisation.
Basic training:
modelNN = learnNN(X, y);
Prediction:
p = predictNN(X_valid, modelNN);
One can use an arbitrary number of hidden layers, different activation functions (currently tanh or sigm), custom regularisation parameter, validation sets, etc. The code does not use any matlab toolboxes, therefore, it is perfect if you do not have the statistics and machine learning toolbox, or if you have an older version of matlab. I use the conjugate gradient algorithm for minimisation borrowed from Andrew Ngs machine learning course. See the github and comments in the code for more documentation.