This book describes how neural networks operate from the
mathematical point of view. As a result, neural networks can be
interpreted both as function universal approximators and
information processors. The book bridges the gap between ideas and
concepts of neural networks, which are used nowadays at an
intuitive level, and the precise modern mathematical language,
presenting the best practices of the former and enjoying the
robustness and elegance of the latter. This book can be used in a
graduate course in deep learning, with the first few parts being
accessible to senior undergraduates. In addition, the book will be
of wide interest to machine learning researchers who are interested
in a theoretical understanding of the subject.
General
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