A Deep Learning Tutorial: From Perceptrons to Deep Networks

In this tutorial, I'll introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java. The single perceptron approach to deep learning has one major drawback: it can only learn linearly separable functions. By the universal approximation theorem, a single hidden layer network with a finite number of neurons can be trained to approximate an arbitrarily random function. You can see a simple (4-2-3 layer) feedforward neural network that classifies the IRIS dataset implemented in Java here through the testMLPSigmoidBP method.