SigridKeydana - Deep Learning and Scientific Computing with R Torch

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.

Series Editors

John M. Chambers, Department of Statistics, Stanford University, California, USA

Torsten Hothorn, Division of Biostatistics, University of Zurich, Switzerland

Duncan Temple Lang, Department of Statistics, University of California, Davis, USA

Hadley Wickham, RStudio, Boston, Massachusetts, USA

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Sigrid Keydana

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For more information about this series, please visit: https://www.crcpress.com/Chapman--HallCRC-The-R-Series/book-series/CRCTHERSER

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First edition published 2023

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CRC Press is an imprint of Taylor & Francis Group, LLC

2023 Sigrid Keydana

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ISBN: 978-1-032-23138-9 (hbk)

ISBN: 978-1-032-23139-6 (pbk)

ISBN: 978-1-003-27592-3 (ebk)

DOI: 10.1201/9781003275923

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This is a book about torch , the R interface to PyTorch. PyTorch, as of this writing, is one of the major deep-learning and scientific-computing frameworks, widely used across industries and areas of research. With torch , you get to access its rich functionality directly from R, with no need to install, let alone learn, Python. Though still young as a project, torch already has a vibrant community of users and developers; the latter not just extending the core framework, but also, building on it in their own packages.

In this text, I'm attempting to attain three goals, corresponding to the book's three major sections.