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.
Author(s): SigridKeydana
Publisher: CRC Press LLC
Year: 2023
Language: English
Pages: 511
Getting Familiar with Torch
chapter 21|2 pages
Overview
chapter 2|2 pages
On torch, and How to Get It
chapter 3|26 pages
Tensors
chapter 4|8 pages
Autograd
chapter 5|6 pages
Function Minimization with autograd
chapter 6|10 pages
A Neural Network from Scratch
chapter 7|6 pages
Modules
chapter 8|12 pages
Optimizers
chapter 9|8 pages
Loss Functions
chapter 10|8 pages
Function Minimization with L-BFGS
chapter 11|4 pages
Modularizing the Neural Network
part II|178 pages
Deep Learning with torch
chapter 96Chapter 12|2 pages
Overview
chapter 13|8 pages
Loading Data
chapter 14|14 pages
Training with luz
chapter 15|20 pages
A First Go at Image Classification
chapter 16|16 pages
Making Models Generalize
chapter 17|12 pages
Speeding up Training
chapter 18|12 pages
Image Classification, Take Two: Improving Performance
chapter 19|20 pages
Image Segmentation
chapter 20|18 pages
Tabular Data
chapter 21|28 pages
Time Series
chapter 22|26 pages
Audio Classification
part III|116 pages
Other Things to do with torch: Matrices, Fourier Transform, and Wavelets
chapter 274Chapter 23|2 pages
Overview
chapter 24|28 pages
Matrix Computations: Least-squares Problems
chapter 25|14 pages
Matrix Computations: Convolution
chapter 26|24 pages
Exploring the Discrete Fourier Transform (DFT)
chapter 27|18 pages
The Fast Fourier Transform (FFT)
chapter 28|28 pages
Wavelets
