Deep learning and scientific computing with R torch Sigrid Keydana.

By:

Keydana, Sigrid [author.]

Material type: Text

TextSeries: Chapman & Hall/CRC the R seriesPublisher: Boca Raton : CRC Press, Taylor & Francis Group, 2023Edition: First editionDescription: 1 online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781003275923

Subject(s):

Additional physical formats: Print version:: Deep learning and scientific computing with R torchDDC classification:

006.31 KEY 23/eng20230323 24678

LOC classification:

Q325.73

Contents:

Overview -- On torch, and how to get it -- Tensors -- Autograd -- Function minimization with autograd -- A neural network from scratch -- Modules -- Optimizers -- Loss functions -- Function minimization with L-BFGS -- Modularizing the neural network -- Loading data -- Training with luz -- A first go at image classification -- Making models generalize -- Speeding up training -- Image classification, take two: Improving performance -- Image segmentation -- Tabular data -- Time series -- Audio classification -- Matrix computations : Least-squares problems -- Matrix computations : convolution -- Exploring the discrete fourier transform (DFT) -- The fast fourier transform (FFT) -- Wavelets.

Summary: "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"-- Provided by publisher.

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Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	COMSATS University Wah Campus	006.31 KEY 24678 (Browse shelf(Opens below))	Available		10004000024678

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"A Chapman & Hall book"

Includes bibliographical references and index.

"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"-- Provided by publisher.

Description based on print version record and CIP data provided by publisher.

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