This book develops the most common neural network architectures. It begins by presenting the netw ork design process and successively studies the Multilayer Perceptron Networks, the Radial Base Network, the ADALINE Networks, the HOPFIELD Networks, the Adaptive Networks, the LEARNING VECTOR QUANTIZATION (LVQ) Network, the Probabilistic Networks, Generalized Regression Networks, Linear Networks and Custom Networks. Examples are developed through MATLAB. MATLAB has the tool Neural Network Toolbox (Deep Leraning toolbox fron release 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.