ARTIFICIAL INTELLIGENCE TOOLS. DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS Examples with MATLAB

MATLAB enables the design of artificial intelligence models through three essential pillars: Machine Learning, Deep Learning and Data Science. Using MATLAB, engineers and other experts have deployed thousands of machine learning applications. MATLAB simplifies the most difficult parts of machine learning. Deep Learning is a subset of machine learning based on artificial neural networks. The process of this learning is called deep because the networks structure consists of having multiple inputs, outputs and hidden layers. Each layer contains units that transform the input data into information, and in this way, the next layer can use it for a certain predictive task. In this way, a machine can learn through its own data processing. Convolution neural networks (CNNs or ConvNets) are essential tools for deep learning, and are especially suited for image recognition. You can construct a CNN architecture, train a network, and use the trained network to predict class labels. You can also extract features from a pre-trained network, and use these features to train a linear classifier. Neural Network Toolbox also enables you to perform transfer learning; that is, retrain the last fully connected layer of an existing CNN on new data. This book develops artificial intelligence tools based in neural networks especialy convolutional neural networks and other deep learning techiques