BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

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

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

Deep Learning and Scientific Computing with R torch (Chapman & Hall/CRC The
Stock photo: cover may vary

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

by Sigrid Keydana

Add to wish list
  • New
New

Description

. Other. New.
Ask the seller a question Add to wish list
A$369.66
A$5.82 Delivery within USA
Standard delivery: 9 to 14 days
More delivery options
Ships from Cold Books (New York, United States)
The BIBLIO Guarantee

Details

  • Title Deep Learning and Scientific Computing with R torch (Chapman & Hall/CRC The R Series)
  • Author Sigrid Keydana
  • Binding Other
  • Condition New
  • Pages 394
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 6396589635
  • ISBN 9781032231389 / 1032231386
  • Weight 1.66 lbs (0.75 kg)
  • Dimensions 9.21 x 6.14 x 0.94 in (23.39 x 15.60 x 2.39 cm)
  • Category Business / Economics / Finance
  • Library of Congress subjects Python (Computer program language), R (Computer program language)
  • Library of Congress Catalogue Number 2022049000
  • Dewey Decimal Code 006.31
  • Quantity available 4

About Cold Books New York, United States

Biblio member since 2012

Terms of Sale: 30 day return guarantee, with full refund including shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Cold Books

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

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

From the publisher

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.

Please wait a moment while we load the requested page.

tracking-