This is a primer written for computer architects in the new and rapidly evolving field of deep learning . It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and ...
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This is a primer written for computer architects in the new and rapidly evolving field of deep learning . It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. It also reviews representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, it also details the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, it presents a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
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Add this copy of Deep Learning for Computer Architects to cart. $56.35, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2017 by Springer.
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Add this copy of Deep Learning for Computer Architects to cart. $67.98, new condition, Sold by Ria Christie Books rated 5.0 out of 5 stars, ships from Uxbridge, MIDDLESEX, UNITED KINGDOM, published 2017 by Springer.
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Good. good book. Trade paperback (US). Glued binding. 109 p. Synthesis Lectures on Computer Architecture. Audience: General/trade. All pages and cover are intact. Pages may have dog-ears. Possible slight loose binding or cocked spine. Cover may have creases, deep chips, scuffs, and scratches. The edges and corners may have a good amount of bumps and wear. Book may contain highlighting/underlining and markings or signed by the previous owner. Book may be Ex Library and have stickers, remainder marks, and library pockets. Used copy so book does not contain unused access codes or have the accompanying CD.
