Название: Deep Learning and Scientific Computing with R torchАвтор: Sigrid Keydana
Издательство: CRC Press
Серия: The R Series
Год: 2023
Страниц: 414
Язык: английский
Формат: pdf (true)
Размер: 10.7 MB
This is a book about torch, the R interface to PyTorch. PyTorch, as of this writing, is one of the major Deep Learning and scientific-computing frameworks, widely used across industries and areas of research. With torch, you get to access its rich functionality directly from R, with no need to install, let alone learn, Python. Though still “young” as a project, torch already has a vibrant community of users and developers; the latter not just extending the core framework, but also, building on it in their own packages. torch 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.
In this text, I’m attempting to attain three goals, corresponding to the book’s three major sections.
The first is a thorough introduction to core torch: the basic structures without whom nothing would work. Even though, in future work, you’ll likely go with higher-level syntactic constructs when possible, it is important to know what it is they take care of, and to have understood the core concepts. What’s more, from a practical point of view, you just need to be “fluent” in torch to some degree, so you don’t have to resort to “trial-and-error-programming” too often.
In the second section, basics explained, we proceed to explore various applications of deep learning, ranging from image recognition over time series and tabular data to audio classification. Here, too, the focus is on conceptual explanation. In addition, each chapter presents an approach you can use as a “template” for your own applications. Whenever adequate, I also try to point out the importance of incorporating domain knowledge, as opposed to the not-uncommon “big data, big models, big compute” approach.
The third section is special in that it highlights some of the non-deep-learning things you can do with torch: matrix computations (e.g., various ways of solving linear-regression problems), calculating the Discrete Fourier Transform, and wavelet analysis. Here, more than anywhere else, the conceptual approach is very important to me. Let me explain.
For one, I expect that in terms of educational background, my readers will vary quite a bit. With R being increasingly taught, and used, in the natural sciences, as well as other areas close to applied mathematics, there will be those who feel they can’t benefit much from a conceptual (though formula-guided!) explanation of how, say, the Discrete Fourier Transform works. To others, however, much of this may be uncharted territory, never to be entered if all goes its normal way. This may hold, for example, for people with a humanist, not-traditionally-empirically-oriented background, such as literature, cultural studies, or the philologies. Of course, chances are that if you’re among the latter, you may find my explanations, though concept-focused, still highly (or: too) mathematical. In that case, please rest assured that, to the understanding of these things (like many others worthwhile of understanding), it is a long way; but we have a life’s time.
Secondly, even though Deep Learning has been “the” paradigm of the last decade, recent developments seem to indicate that interest in mathematical/domain-based foundations is (again – this being a recurring phenomenon) on the rise.
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.
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