Book Review: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning | AI Deep Learning - insideBIGDATA

We’re seeing a rising number of new books on the mathematics of data science, machine learning, AI and deep learning, which I view as a very positive trend because of the importance for data scientists to understand the theoretical foundations for these technologies by Daniel Gutierrez.

Artificial Intelligence Engines: 

A Tutorial Introduction to 

the Mathematics of Deep Learning

In the coming months, I plan to review a number of these titles, but for now, I’d like to introduce a real 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 Vision and Computational Neuroscience at the University of Sheffield, England.

The author provides a GitHub repo containing Python code examples based on the topics found in the book. You can also download Chapter 1 for free HERE.

The main reason why I like this book so much is because of its tutorial format. It’s not a formal text on the subject matter, but rather a relatively short and succinct (only 200 pages) guide book for understanding the mathematical fundamentals of deep learning...

I would recommend this tutorial to any data scientist wishing to get quickly up to speed with the foundations of arguably the most important technology discipline today. The best time to move ahead with your education is now with this great resource!

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Source: insideBIGDATA