Book Review: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning


We’re seeing a rising variety of new books on the arithmetic of information science, machine studying, AI and deep studying, which I view as a really optimistic pattern due to the significance for information scientists to grasp the theoretical foundations for these applied sciences. Within the coming months, I plan to evaluate quite a lot of these titles, however for now, I’d wish to introduce an actual gem: “Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning,” by James V. Stone, 2019 Sebtel Press. Dr. Stone is an Honorary Reader in Imaginative and prescient and Computational Neuroscience on the College of Sheffield, England.

The writer offers a GitHub repo containing Python code examples based mostly on the subjects discovered within the guide. You can even obtain Chapter 1 free of charge HERE.

The principle cause why I like this guide a lot is due to its tutorial format. It’s not a proper textual content on the subject material, however somewhat a comparatively quick and succinct (solely 200 pages) information guide for understanding the mathematical fundamentals of deep studying. I used to be in a position to skim it’s content material in about 2 hours, and a radical studying could possibly be achieved in just a few days relying in your math background. I’m engaged on a deep dive now, with word pad and pencil in hand, as I see the guide offering a well timed refresh of the mathematics I first noticed in grad faculty. What you achieve ultimately is a well-balanced formulation for the way deep studying works underneath the hood. Information scientists can “get by” with out the mathematics when working with deep studying, however a lot of the method turns into guess work with out the insights that the mathematics brings to the desk.

The guide contains the next chapters:

  1. Synthetic Neural Networks
  2. Linear Associative Networks
  3. Perceptrons
  4. The Backpropagation Algorithm
  5. Hopfield Nets
  6. Boltzmann Machines
  7. Deep RBMs
  8. Variational Autoencoders
  9. Deep Backprop Networks
  10. Reinforcement Studying
  11. The Emperor’s New AI?

In every chapter, you’ll discover all of the foundational arithmetic to help the subject. You’ll must know some primary Calculus, linear algebra, and partial totally different equations to maneuver ahead, however Stone embody a number of helpful Appendices that will help you alongside: mathematical symbols, a vector and matrix tutorial, most probability estimation, and Baye’s Theorem.

The chapters additionally function necessary algorithms introduced in simple to grasp algorithm pseudo-code (there isn’t any normal on this regard, however Stone’s rendition could be very simple). One large function of the guide is its full bibliography of seminal books and papers which have contributed to the advance of deep studying over time. The guide provides a historic perspective for the way deep studying advanced in many years previous, coupled with necessary papers alongside the best way. Utilizing this guide as a information, you’ll have an entire and detailed roadmap for deep studying and the lengthy, usually winding path it has taken.

I particularly like Stone’s remedy of gradient descent, and again propagation. Should you’ve ever been confused about these constructing blocks of deep studying, this guide’s tutorial on these topics will provide you with a pleasant kick-start.

Matters like Hopfield Nets and Boltzmann Machines are included to supply a historic lineage. Traditionally, Hopfield Nets (circa 1982) preceded backprop networks. The Hopfield internet is necessary as a result of it’s based mostly on the mathematical equipment of a department of physics known as statistical mechanics, which enabled studying to be interpreted when it comes to vitality features. Hopefield nets led on to Boltzmann machines, which signify an necessary stepping stone to fashionable deep studying methods. On this respect, it’s helpful to see how deep studying has grown into its present state.

I might advocate this tutorial to any information scientist wishing to get rapidly in control with the foundations of arguably crucial expertise self-discipline immediately. One of the best time to maneuver forward together with your training is now with this nice useful resource!

Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Information Scientist for insideBIGDATA. Along with being a tech journalist, Daniel is also a marketing consultant in information scientist, writer, educator and sits on quite a lot of advisory boards for numerous start-up firms. 

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