Apress, 2019. — 640 p. — ISBN-13 (electronic): 978-1-4842-4421-0. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of...
Packt Publishing Ltd., 2019. — 385 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...
CreateSpace, 2016. — 222 p. A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take...
O’Reilly Media, 2019. — 228 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...
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