Deep Learning and Scientific Computing with R torch  book cover
1st Edition

Deep Learning and Scientific Computing with R torch

  • Available for pre-order on March 27, 2023. Item will ship after April 17, 2023
ISBN 9781032231396
April 17, 2023 Forthcoming by Chapman & Hall
424 Pages 91 B/W Illustrations

FREE Standard Shipping
SAVE $20.99
was $69.95
USD $48.96

Prices & shipping based on shipping country


Book Description

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.

Table of Contents

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

View More



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.