## ABSTRACT

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 I|94 pages

Getting Familiar with Torch

part II|178 pages

Deep Learning with torch

part III|116 pages

Other Things to do with torch: Matrices, Fourier Transform, and Wavelets