This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and desc

Neural networks have been widely used in many applications, such as image classification and understanding, language processing, and control of autonomous syste

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear alge

For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.

Selected as the Michigan Council of Teachers of Mathematics winter book club book! Rich tasks, collaborative work, number talks, problem-based learning, direct