Deep Learning and Scientific Computing with R torch

ISBN-13: 9781032231396 / Angielski / Miękka / 2023 / 404 str.

Sigrid Keydana

Opis

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.

Spis treści

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

O autorze

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.