Why Machines Learn: The Elegant Math Behind Modern AI

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Why Machines Learn by Anil Ananthaswamy is a great book; here's why:
The book's pattern is that each chapter starts with a relevant story, an overview, some math foundations, and valuable examples. There are many equations, ~1000, but stick with it. Ignore Stephen Hawking's advice: "Someone told me that each equation I included in the book would halve the sales." An interested high school student can work through the book; Anil has exceptional explanations and examples.

My first pass through the book took a month; now, I want to do a second pass using Mathematica to build the equations into a framework. As Feynman said, "I understand what I build." I used a hybrid approach, listening to the book while making notes in a printed copy. This worked well for me, as I didn't want to skip anything.

The author brings out the personalities behind ML beyond the popular science level; their equations also speak for them. Humor throughout: "In high dimensional space, no one can hear you scream." Julie Delon

Favorites:
Story - Al-Hazen & vision science
Math - Optimization & Lagrange Multipliers
Concept 2nd descent of bias-variance curve
Example Consciousness & Anesthesia EEG PCA

I wish the second half of the book had more examples. The author could expand the last chapters and epilogue into another book. There are good references and a helpful index. A bibliography and suggestions on What's Next would be beneficial.

This book has inspired me to delve deeper into understanding ML; I want to comprehend the changes ML brings to our world. Anil has helped me bootstrap myself.