Deep Learning for Computer Architects Book [PDF] Download

Download the fantastic book titled Deep Learning for Computer Architects written by Brandon Reagen, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Deep Learning for Computer Architects", which was released on 31 May 2022. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Technology & Engineering genre.

Summary of Deep Learning for Computer Architects by Brandon Reagen PDF

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.


Detail About Deep Learning for Computer Architects PDF

  • Author : Brandon Reagen
  • Publisher : Springer Nature
  • Genre : Technology & Engineering
  • Total Pages : 109 pages
  • ISBN : 3031017560
  • PDF File Size : 8,9 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Deep Learning for Computer Architects

Deep Learning for Computer Architects
  • Publisher : Springer Nature
  • File Size : 20,7 Mb
  • Release Date : 31 May 2022
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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

AI for Computer Architecture

AI for Computer Architecture
  • Publisher : Springer Nature
  • File Size : 50,9 Mb
  • Release Date : 31 May 2022
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Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the

Data Orchestration in Deep Learning Accelerators

Data Orchestration in Deep Learning Accelerators
  • Publisher : Springer Nature
  • File Size : 20,6 Mb
  • Release Date : 31 May 2022
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This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications

Deep Learning: Concepts and Architectures

Deep Learning: Concepts and Architectures
  • Publisher : Springer Nature
  • File Size : 33,8 Mb
  • Release Date : 29 October 2019
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This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a

Deep Learning for Data Architects

Deep Learning for Data Architects
  • Publisher : BPB Publications
  • File Size : 31,5 Mb
  • Release Date : 16 August 2023
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A hands-on guide to building and deploying deep learning models with Python KEY FEATURES ● Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning

Deep Learning Systems

Deep Learning Systems
  • Publisher : Springer Nature
  • File Size : 43,7 Mb
  • Release Date : 31 May 2022
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This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is

The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook
  • Publisher : Packt Publishing Ltd
  • File Size : 33,9 Mb
  • Release Date : 21 January 2022
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Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning

Embedded Deep Learning

Embedded Deep Learning
  • Publisher : Springer
  • File Size : 32,7 Mb
  • Release Date : 23 October 2018
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This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help

Deep Learning: Hardware Design

Deep Learning: Hardware Design
  • Publisher : Unknown Publisher
  • File Size : 40,9 Mb
  • Release Date : 21 July 2020
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2nd edition. With the Convolutional Neural Network (CNN) breakthrough in 2012, the deep learning is widely appliedto our daily life, automotive, retail, healthcare and finance. In 2016, Alpha Go with ReinforcementLearning (RL)

The Deep Learning Architect's Handbook

The Deep Learning Architect's Handbook
  • Publisher : Packt Publishing Ltd
  • File Size : 37,9 Mb
  • Release Date : 29 December 2023
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Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret