This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: * Basic classification and regression with perceptrons * Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training * Multi-Layer Perceptrons for learning from descriptors, and de-noising data * Recurrent neural networks for learning from sequences * Convolutional neural networks for learning from images * Bayesian optimization for tuning deep learning architectures Each of these areas...