This text serves as a primer for computer architects in a new and rapidly evolving field.

This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.

This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.

Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures.

This book takes you systematically through the core mathematical concepts, linear algebra, and Bayesian inference, all from a deep learning perspective.

This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project.

The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

Putting it Together: Architecting a DNN Accelerator -- Design Flow -- Target Specs and Constraints -- Architecture Design -- Microarchitecture and Implementation -- Mapping -- Example Design Walk Through -- Data Center Class Example -- Edge ...