Deep Learning and Scientific Computing with R torch
Welcome!
This is the on-line edition of Deep Learning and Scientific Computing with R torch, written by Sigrid Keydana. Visit the GitHub repository for this site, or buy a physical copy from the publisher, CRC Press. You’ll also find the book at the usual outlets, e.g., Amazon.
This on-line work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Preface
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.
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 (Consider, for example, the Geometric Deep Learning approach, systematically explained in Bronstein et al. (2021), and conceptually introduced in Beyond alchemy: A first look at geometric deep learning.) In the future, I assume that we’ll likely see more and more “hybrid” approaches that integrate deep-learning techniques and domain knowledge. The Fourier Transform is not going away.
Last but not least, on this topic, let me make clear that, of course, all chapters have torch code. In case of the Fourier Transform, for example, you’ll see not just the official way of doing this, using dedicated functionality, but also, various ways of coding the algorithm yourself – in a surprisingly small number of lines, and with highly impressive performance.
This, in a nutshell, is what to expect from the book. Before I close, there is one thing I absolutely need to say, all the more since even though I’d have liked to, I did not find occasion to address it much in the book, given the technicality of the content. In our societies, as adoption of machine/deep learning (“AI”) is growing, so are opportunities for misuse, by governments as well as private organizations. Often, harm may not even be intended; but still, outcomes can be catastrophic, especially for people belonging to minorities, or groups already at a disadvantage. Like that, even the inevitable, in most of today’s political systems, drive to make profits results in, at the very least, societies imbued with highly questionable features (think: surveillance, and the “quantification of everything”); and most likely, in discrimination, unfairness, and severe harm. Here, I cannot do more than draw attention to this problem, point you to an introductory blog post that perhaps you’ll find useful: Starting to think about AI Fairness, and just ask you to, please, be actively aware of this problem in public life as well as your own work and applications.
Finally, let me end with saying thank you. There are far too many people to thank that I could ever be sure I haven’t left anyone out; so instead I’ll keep this short. I’m extremely grateful to my publisher, CRC Press (first and foremost, David Grubbs and Curtis Hill) for the extraordinarily pleasant interactions during all of the writing and editing phases. And very special thanks, for their support related to this book as well as their respective roles in the process, go to Daniel Falbel, the creator and maintainer of torch, who in-depth reviewed this book and helped me with many technical issues; Tracy Teal, my manager, who supported and encouraged me in every possible way; and Posit (formerly, RStudio), my employer, who lets me do things like this for a living.
Sigrid Keydana