Deep Learning and Scientific Computing with R Torch

✍ Scribed by SigridKeydana

Publisher: CRC Press LLC
Year: 2023
Tongue: English
Leaves: 511
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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

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

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