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E-grāmata: Deep Learning and Scientific Computing with R torch

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  • Formāts: 414 pages
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 05-Apr-2023
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781000862935
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Deep Learning and Scientific Computing with R torchPalielināt
  • Formāts: 414 pages
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 05-Apr-2023
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781000862935
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This book aims to be useful to (almost) everyone.  Deep Learning and Scientific Computing with R Torch provides 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.



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.

Recenzijas

"The book is very well written and easy to follow with plenty of illustrations and explanations via examples and codes. I have learned a lot from the book and believe that many R users can greatly benefit from it as well even without an extensive machine learning background."

- Yang Ni, Texa A&M University, U.S.A, The MAerican Statistician, April 2024

Part
1. Getting familiar with torch
1. Overview
2. On torch, and how to
get it
3. Tensors
4. Autograd
5. Function minimization with autograd
6. A
neural network from scratch
7. Modules
8. Optimizers
9. Loss functions
10.
Function minimization with L-BFGS
11. Modularizing the neural network Part
2.
Deep learning with torch
12. Overview
13. Loading data
14. Training with luz
15. A first go at image classification
16. Making models generalize
17.
Speeding up training
18. Image classification, take two: Improving
performance
19. Image segmentation
20. Tabular data
21. Time series
22. Audio
classification Part
3. Other things to do with torch: Matrices, Fourier
Transform, and Wavelets
23. Overview
24. Matrix computations: Least-squares
problems
25. Matrix computations: Convolution
26. Exploring the Discrete
Fourier Transform (DFT)
27. The Fast Fourier Transform (FFT)
28. Wavelets
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.
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