Deep Learning and Scientific Computing with R torch (Chapman & Hall/CRC The R Series)

Keydana, Sigrid

ISBN 10: 1032231394 / ISBN 13: 9781032231396

Published by Chapman and Hall/CRC, 2023

About this Item

424 pages. 9.19x6.13x0.94 inches. In Stock.

Seller Inventory # x-1032231394

Bibliographic Details

• Title: Deep Learning and Scientific Computing with R torch (Chapman & Hall/CRC The R Series)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2023
• Language: English
• ISBN 10: 1032231394
• ISBN 13: 9781032231396
• Binding: Paperback
• Condition: Brand New
• Weight: 0.73 kilograms

About this title

Synopsis

torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++.

Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:

• Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch
• Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classification
• Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with.

Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.

About the Author

Sigrid Keydana is an Applied Researcher at Posit (formerly RStudio, PBC). She has a background in the humanities, psychology, and information technology, and is passionate about explaining complex concepts in a concepts-first, comprehensible way.