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100%: Reagen, Brandon: Deep Learning for Computer Architects (Hardback) (ISBN: 9781681732190) MORGAN and CLAYPOOL, in Englisch, Broschiert.
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100%: Brandon Reagen, David Brooks, Gu-Yeon Wei, Margaret Martonosi, Paul Whatmough, Robert Adolf: Deep Learning for Computer Architects (ISBN: 9781681731728) 2017, in Englisch, auch als eBook.
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100%: Reagen, Brandon; Adolf, Robert; Whatmough, Paul: Deep Learning for Computer Architects (Synthesis Lectures on Computer Architecture) (ISBN: 9781627057288) 2017, Morgan Claypool Publishers, United States, in Englisch, Taschenbuch.
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100%: Brooks, David; Martonosi, Margaret; Reagen, Brandon; Adolf, Robert; Whatmough, Paul; Wei, Gu-Yeon: Deep Learning for Computer Architects (ISBN: 9781627059855) Morgan & Claypool Publishers, in Englisch, auch als eBook.
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Deep Learning for Computer Architects (Hardback) - 5 Angebote vergleichen
Bester Preis: € 43,17 (vom 05.12.2017)1
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Deep Learning for Computer Architects (Hardback) (2017)
EN HC NW RP
ISBN: 9781681732190 bzw. 168173219X, in Englisch, Morgan Claypool, gebundenes Buch, neu, Nachdruck.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Free shipping.
Von Händler/Antiquariat, The Book Depository US [58762574], London, United Kingdom.
Language: English . Brand New Book ***** Print on Demand *****. 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. This text serves as a primer for computer architects in a new and rapidly evolving field. We 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 emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail 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, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Von Händler/Antiquariat, The Book Depository US [58762574], London, United Kingdom.
Language: English . Brand New Book ***** Print on Demand *****. 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. This text serves as a primer for computer architects in a new and rapidly evolving field. We 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 emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail 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, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
2
Deep Learning for Computer Architects (2017)
EN HC NW
ISBN: 9781681732190 bzw. 168173219X, in Englisch, Morgan & Claypool, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Usually ships in 24 hours, free shipping for AmazonPrime only. Regular USD 4.98.
Von Händler/Antiquariat, Amazon.com.
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. This text serves as a primer for computer architects in a new and rapidly evolving field. We 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 emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail 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, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context., Hardcover, Label: Morgan & Claypool, Morgan & Claypool, Product group: Book, Published: 2017-08-22, Studio: Morgan & Claypool, Sales rank: 662373.
Von Händler/Antiquariat, Amazon.com.
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. This text serves as a primer for computer architects in a new and rapidly evolving field. We 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 emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail 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, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context., Hardcover, Label: Morgan & Claypool, Morgan & Claypool, Product group: Book, Published: 2017-08-22, Studio: Morgan & Claypool, Sales rank: 662373.
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Deep Learning for Computer Architects (2017)
EN NW RP
ISBN: 9781681732190 bzw. 168173219X, in Englisch, MORGAN and CLAYPOOL, neu, Nachdruck.
Von Händler/Antiquariat, Pbshop [61989342], Wood Dale, IL, U.S.A.
New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Deep Learning for Computer Architects (2017)
EN NW RP
ISBN: 9781681732190 bzw. 168173219X, in Englisch, MORGAN and CLAYPOOL, neu, Nachdruck.
Von Händler/Antiquariat, Books2Anywhere [190245], Fairford, GLOS, United Kingdom.
New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000.
New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000.
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Deep Learning for Computer Architects (2000)
EN PB NW
ISBN: 9781681732190 bzw. 168173219X, in Englisch, MORGAN & CLAYPOOL, Taschenbuch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, plus shipping, Shipping area: EUR.
Von Händler/Antiquariat, Paperbackshop International, GLOS, Fairford, [RE:4].
Softcover.
Von Händler/Antiquariat, Paperbackshop International, GLOS, Fairford, [RE:4].
Softcover.
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