Deep Learning for Computer Architects (Synthesis Lectures on Computer Architecture) - Image 1

£42.69

Springer Deep Learning for Computer Architects (Synthesis Lectures on Computer Architecture)

Name: Deep Learning for Computer Architects (Synthesis Lectures on Computer Architecture)
Brand: Springer
Price: 42.84 GBP
Availability: InStock

Architecture & Microprocessors

Price History & Forecast

Last 27 days • 27 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 27 days • 1 price levels

Days at Price

Price Analysis

Most common price: £43 (27 days, 100.0%)

Price range: £43 - £43

Price levels: 1 different prices over 27 days

Description

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 workloadsthemselves, 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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3031006283
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 22 August 2017
Listed Since: 01 June 2022

Barcode

No barcode data available

Springer Deep Learning for Computer Architects (Synthesis Lectures on Computer Architecture)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production (Synthesis Lectures on Computer Architecture)

Efficient Processing of Deep Neural Networks (Synthesis Lectures on Computer Architecture)

Springer Deep Learning: Concepts and Architectures 866 Book

Springer Deep Learning: Concepts and Architectures 866 Book

Data Orchestration in Deep Learning Accelerators (Synthesis Lectures on Computer Architecture)

Learning Deep Architectures for AI (Foundations and Trends® in Machine Learning)

Neural Networks and Deep Learning: A Textbook

Neural Networks and Deep Learning: A Textbook

Springer Deep Neural Evolution - Deep Learning and EC Book

Neural Networks and Deep Learning: A Textbook

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Development and Analysis of Deep Learning Architectures: 867 (Studies in Computational Intelligence, 867)

Deep Learning Networks: Design, Development and Deployment

Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)

Fundamentals of Machine Learning and Deep Learning in Medicine

Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)

Deep Learning: Foundations and Concepts

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow

Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications: 18 (Learning and Analytics in Intelligent Systems, 18)

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Domain-Specific Computer Architectures for Emerging Applications: Machine Learning and Neural Networks

Graph Neural Networks: Foundations, Frontiers, and Applications