Deep Learning and Scientific Computing with R torch (Paperback)

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.

torch是PyTorch的R語言版本，PyTorch是工業界和研究領域中最常用的兩個深度學習框架之一。它也是一個在科學計算中非常優秀的工具。torch完全使用R和C/C++編寫。

儘管作為一個項目仍然相對年輕，R的torch已經擁有一個充滿活力的用戶和開發者社區。經驗表明，torch的用戶來自各種不同的背景。本書旨在對（幾乎）所有人有用。從全球的角度來看，它的目的有三個：

1. 提供對torch基礎知識的全面介紹 - 通過仔細解釋底層概念和思想，並提供足夠的示例讓讀者能夠熟練使用torch。
2. 再次著重於概念解釋，展示如何在深度學習應用中使用torch，從圖像識別到時間序列預測再到音頻分類。
3. 提供一個以概念為先、讀者友好的介紹選定的科學計算主題（即矩陣計算、離散傅立葉變換和小波），並附帶可以互動的torch代碼。

《使用R的深度學習和科學計算torch》是以第一手的技術專業知識撰寫的，並以引人入勝、有趣易讀的方式呈現。

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.

Sigrid Keydana是Posit（前身為RStudio, PBC）的應用研究員。她在人文學科、心理學和資訊技術方面具有背景，並熱衷於以概念為先、易於理解的方式解釋複雜概念。