Deep Learning Architectures, A Mathematical Method

This book describes how neural networks operate from the mathematical point of view. As an outcome, neural networks can be translated both as function universal approximators and information processors. The book bridges the space between ideas and ideas of neural networks, which are used nowadays at an intuitive level, and the precise contemporary mathematical language, presenting the best practices of the former and enjoying the effectiveness and sophistication of the latter. This book can be used in a graduate course in deep learning, with the very first couple of parts being accessible to senior undergrads. In addition, the book will be of large interest to artificial intelligence researchers who are interested in a theoretical understanding of the topic.

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