Deep learning and scientific computing with R torch / Sigrid Keydana.
| Author/creator | Keydana, Sigrid |
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton : CRC Press, Taylor & Francis Group, 2023. |
| Description | 1 online resource |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Subjects |
| Series | Chapman & Hall/CRC the R series |
| 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. |
| Abstract | "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. |
| General note | "A Chapman & Hall book" |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Source of description | Description based on print version record and CIP data provided by publisher. |
| Issued in other form | Print version: Keydana, Sigrid. Deep learning and scientific computing with R torch First edition. Boca Raton : CRC Press, Taylor & Francis Group, 2023 9781032231389 |
| Genre/form | Electronic books. |
| LCCN | 2022049001 |
| ISBN | 9781003275923 (ebook) |
| ISBN | (hardback) |
| ISBN | (paperback) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |