Why Machines Learn: The Elegant Math Behind Modern AI
Author:Anil Ananthaswamy
Edition:1st Edition
The mathematical foundations of modern artificial intelligence are explored by Anil Ananthaswamy, explaining how machines identify patterns, make predictions, and learn from data. It introduces key concepts such as probability, optimization, linear algebra, and neural networks, showing how these ideas power machine learning systems while making complex mathematics accessible and intuitive.
Chapters
- Chapter 1: Desperately Seeking Patterns→
- Chapter 2: We Are All Just Numbers Here...→
- Chapter 3: The Bottom of the Bowl→
- Chapter 4: In All Probability→
- Chapter 5: Birds of a Feather→
- Chapter 6: There's Magic in Them Matrices→
- Chapter 7: The Great Kernel Rope Trick→
- Chapter 8: With a Little Help from Physics→
- Chapter 9: The Man Who Set Back Deep Learning (Not Really)→
- Chapter 10: The Algorithm That Put Paid to a Persistent Myth→
- Chapter 11: The Eyes of a Machine→
- Chapter 12: Terra Incognita→
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