Sigrid Keydana

Deep Learning and Scientific Computing with R torch

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Chapman and Hall/CRC
• Seitenzahl: 414
• Erscheinungstermin: 5. April 2023
• Englisch
• Abmessung: 240mm x 161mm x 27mm
• Gewicht: 784g
• ISBN-13: 9781032231389
• ISBN-10: 1032231386
• Artikelnr.: 67262358

Autorenporträt

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.

Inhaltsangabe

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

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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

Rezensionen

"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