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Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production

Posted By: AvaxGenius
Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production

Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production by Andres Rodriguez
English | PDF(True) | 2020 | 267 Pages | ISBN : 1681739682 | 5.4 MB

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.

Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale

Posted By: Underaglassmoon
Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale

Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale
Morgan & Claypool | English | 2021 | ISBN-10: 1681739682 | 256 pages | PDF | 5.39 MB

by Andres Rodriguez (Author)
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications

An Introduction to Statistical Learning: with Applications in R (Corrected at 8th printing 2017)

Posted By: AvaxGenius
An Introduction to Statistical Learning: with Applications in R (Corrected at 8th printing 2017)

An Introduction to Statistical Learning: with Applications in R by Gareth James
English | PDF,EPUB (True) | 2013 (Corrected at 8th printing 2017) | 434 Pages | ISBN : 1461471370 | 14.62 MB

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.