Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production (Synthesis Lectures on Computer Architecture) - Image 1

£31.47

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

Name: Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production (Synthesis Lectures on Computer Architecture)
Brand: Springer
Price: 31.47 GBP
Availability: InStock

Architecture & Microprocessors

This is the most expensive it has ever been. Walk away.

£31 today · previous high £31 · all-time low £30

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 71 days · 71 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 71 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £31 (61 days, 85.9%)

Price range: £30 - £31

Price levels: 2 different prices over 71 days

Description

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 slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to bettercollaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3031006410
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 26 October 2020
Listed Since: 01 June 2022

Barcode

No barcode data available

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

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

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

Manning Engineering Deep Learning Systems - Software Guide

Deep Learning at Scale: At the Intersection of Hardware, Software, and Data

Scalable and Distributed Machine Learning and Deep Learning Patterns (Advances in Computational Intelligence and Robotics)

Springer - Parallel and Distributed Computational Intelligence 269

AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch

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

MLOps Engineering at Scale: Deploying Pytorch Models on Aws

Springer Deep Learning: Algorithms and Applications 865 Book

Modern Deep Learning Design and Application Development: Versatile Tools to Solve Deep Learning Problems

Supercomputing for Artificial Intelligence - AI Scaling Guide

Springer Deep Learning: Concepts and Architectures 866 Book

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics

Mathematical Foundations of Deep Learning Models and Algorithms (Graduate Studies in Mathematics)

Deep Learning: Convergence to Big Data Analytics (SpringerBriefs in Computer Science)

Springer Deep Learning: Concepts and Architectures 866 Book

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

The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

Principles and Labs for Deep Learning

Deep Learning Design Patterns

Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems: 1

Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms (Advances in Systems Analysis, Software Engineering, and High Performance Computing)

Penguin Random House Deep Learning (Adaptive Computation and Machine Learning series)