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230519s2023 flua b 001 0 eng |
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▼a 2022049000
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▼a 9781032231389
▼q (hardback)
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▼a 9781032231396
▼q (paperback)
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▼z 9781003275923
▼q (ebook)
|
| 035 |
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▼a (KERIS)REF000020185025
|
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▼a DLC
▼b eng
▼e rda
▼c DLC
▼d 211009
|
| 042 |
|
▼a pcc
|
| 050 |
0
0
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▼a Q325.73
▼b .K49 2023
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▼a 006.3/1
▼2 23
|
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▼a 006.31
▼2 DDCK
|
| 090 |
|
▼a 006.31
▼b K44d
|
| 100 |
1
|
▼a Keydana, Sigrid.
|
| 245 |
1
0
|
▼a Deep learning and scientific computing with R torch /
▼c Sigrid Keydana.
|
| 250 |
|
▼a 1st ed.
|
| 260 |
|
▼a Boca Raton :
▼b CRC Press,
▼c 2023.
|
| 264 |
1
|
▼a Boca Raton :
▼b CRC Press,
▼c 2023.
|
| 300 |
|
▼a xix, 393 p. :
▼b ill. ;
▼c 24 cm.
|
| 336 |
|
▼a text
▼b txt
▼2 rdacontent
|
| 337 |
|
▼a unmediated
▼b n
▼2 rdamedia
|
| 338 |
|
▼a volume
▼b nc
▼2 rdacarrier
|
| 490 |
1
|
▼a The R series
|
| 504 |
|
▼a Includes bibliographical references and index.
|
| 505 |
0
|
▼a 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.
|
| 520 |
|
▼a "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.
|
| 650 |
0
|
▼a Deep learning (Machine learning).
|
| 650 |
0
|
▼a Science
▼x Mathematics
▼x Data processing.
|
| 650 |
0
|
▼a Python (Computer program language).
|
| 650 |
0
|
▼a R (Computer program language).
|
| 830 |
0
|
▼a R series.
|
| 945 |
|
▼a ITMT
|
