Understanding Deep Learning

During the winter break, I decided to devote some time to reading and stumbled upon “Understanding Deep Learning” (UDL) by Simon J.D. Prince. While searching for the most authoritative and standard textbooks on deep learning, I was soon struck with the realization that having updated information in a rapidly changing field is important. This understanding steered me towards Prince’s book, setting it apart from older, more established works like Ian Goodfellow’s “Deep Learning.”

In a field like this, where progress is swift and unrelenting, a book that’s been recently updated becomes a crucial source of knowledge. It’s as if the hard work has already been done for you, with the intricate and growing realm of deep learning knowledge being efficiently compiled and distilled into a singular, accessible guide. This is the essence of UDL. The book presents a straightforward, no-frills view of the most current ideas, trends, and technological breakthroughs in AI. In the next few paragraphs, I’ll talk about this book and its contents in brief, with cool illustrations.

Overview of chapters

UDL’s Chapter 1 kicks off with supervised learning, then quickly jumps to unsupervised learning and generative models, showcasing current cool stuff like inpainting and GPT-3 conditional text synthesis. It introduces latent variables (because, why not?) and has some fun with a teddy bear on a skateboard (DALL-E 2). This chapter is visually packed and even sneaks in a bit on reinforcement learning and ethics — kind of a ‘be responsible’ nod, a la Andrew Ng. It’s a solid base for newbies and feels like home for AI techbro. (And hey, it even includes a reading list for all these topics!)

Chapter 2 of UDL, titled ‘Supervised Learning,’ is concise. It provides an overview of supervised learning and delves into a linear regression example, wrapping up quickly.

Chapter 3, ‘Shallow Neural Networks,’ starts with a neural network example and covers the Universal Approximation Theorem. It also discusses multivariate inputs and outputs, providing a comprehensive overview of shallow neural networks, including terminology, and concludes with summary and notes. (did I tell that the illustrations are just beautiful?)

Chapter 4, ‘Deep Neural Networks,’ takes the reader further into the complexities of AI. It begins with composing neural networks, transitions into the nuances of deep networks, and discusses deep neural networks in detail, including matrix notation and a comparison between shallow and deep neural networks. This chapter culminates with a summary, offering a clear understanding of the subject.

The middle chapters (5 to 8) of UDL are particularly notable for their practical orientation. These chapters cover critical elements such as loss functions, model training, and performance evaluation. Chapter 8 introduces and discusses newer concepts like double descent (on how bias-variance tradeoff is a thing of the past in overparameterized models that we can have today).

UDL then steers into advanced territory, encompassing chapters on convolutional and residual networks, transformers, and graph neural networks (Chapters 10 to 13). It’s here that you (or at least I did) realize these topics are vast fields in themselves, deserving entire books. The four chapters in UDL are impressively concise and provide a solid primer, but they also make it clear that dedicated, in-depth reading in each area would be beneficial. For anyone eager to dive deeper, the notes section is a treasure trove. It’s brilliantly organized, subtopic-wise, and includes references to pivotal papers, allowing readers to explore these subjects at the source level (again, the illustrations of architectures, computational graphs, and examples are just fantastic in these chapters).

The latter part of UDL (Chapters 14 to 19) delves into unsupervised learning, generative models, and deep reinforcement learning, addressing complex topics such as GANs, normalizing flows, variational autoencoders, diffusion models. These chapters not only offer an in-depth understanding of these advanced methods but also highlight their practical applications across various fields. (be prepared for even more math here, but rest assured, you can do it!)

UDL concludes with thought-provoking discussions in its final chapters. Chapter 20 explores the fundamental question of why deep learning is so effective, and Chapter 21 rounds off the book with a critical examination of the ethical dimensions of AI. This comprehensive approach, encompassing both the technical intricacies and ethical aspects, positions UDL as an all-encompassing resource for anyone eager to grasp the current landscape and future directions of deep learning.

Whom Is This Book For?

UDL starts off as an undergraduate-level textbook, but it doesn’t stop there. It’s crafted to guide readers from the foundational aspects of the field right through to the more complex and advanced topics. The book breaks down these sophisticated concepts with clear explanations and practical examples, making them more approachable. (And for those who want to dive even deeper, the notes and reading lists at the end of each chapter are a rich resource for further exploration. pure gold.)

What sets UDL apart is its broad appeal. It’s not just for undergrad students; it offers insights and knowledge that cater to a wide audience. Whether you’re a beginner just starting to explore AI, a student delving deeper into your studies, a developer handling AI in the real world, or a researcher pushing the boundaries of what’s possible, UDL has something for you. It ties together the vast area of deep learning into a narrative that is both understandable and engaging. (Plus, the visual aspects of the book add an extra layer of learning, making complex topics not only accessible but also visually engaging.)

udlbook by Simon J.D. Prince Published by MIT Press Dec 5th 2023._udlbook.github.io

PDF version of the book is free. Do support the author by getting a copy for yourself and your friends. Happy holidays!