DEEP LEARNING FOR COMPUTER ARCHITECTS (SYNTHESIS LECTURES By Paul Whatmough
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DEEP LEARNING FOR COMPUTER ARCHITECTS (SYNTHESIS LECTURES By Paul Whatmough
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Numéro de l'objet eBay :336018753630
Caractéristiques de l'objet
- État
- Bon état
- Commentaires du vendeur
- ISBN-10
- 1627057285
- Book Title
- Deep Learning for Computer Architects (Synthesis Lectures on
- ISBN
- 9781627057288
À propos de ce produit
Product Identifiers
Publisher
Morgan & Claypool Publishers
ISBN-10
1627057285
ISBN-13
9781627057288
eBay Product ID (ePID)
240299249
Product Key Features
Number of Pages
123 Pages
Language
English
Publication Name
Deep Learning for Computer Architects
Publication Year
2017
Subject
Systems Architecture / General, Intelligence (Ai) & Semantics, Neural Networks
Type
Textbook
Subject Area
Computers
Series
Synthesis Lectures on Computer Architecture Ser.
Format
Trade Paperback
Dimensions
Item Height
0.3 in
Item Weight
8 Oz
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Intended Audience
Trade
Illustrated
Yes
Table Of Content
Preface Introduction Foundations of Deep Learning Methods and Models Neural Network Accelerator Optimization: A Case Study A Literature Survey and Review Conclusion Bibliography Authors' Biographies
Synopsis
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., A primer for computer architects in a new and rapidly evolving field. The authors review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that have emerged in the last decade., 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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- e***l (804)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéLow cost, fast shipping, probably in good condition, A+ ebayer.
- j***j (18)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéThe item I ordered was listed as “mint condition” and it may have been but was severely damaged when received. The packaging was just a plastic bag with no protection and the shipping time was longer than the listed dates. The seller is extremely slow in responding to messages (averaging one reply per DAY) and offered an insulting low discount to keep the damaged item they listed as like new and packaged badly. This has been one of the WORST experiences I have had on eBay! DO NOT RECOMMEND!
- s***g (103)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéMy copy was in pristine condition. I couldn't be happier, thanks again!
- h***s (68)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéMy daughter loves it! Arrived as described, thank you!