Algorithmic Overfitting

By Justin Z. MartinezHere’s how models memorize noise

Length1h 4m

About this book

Summary

Algorithmic Overfitting explores why learning systems—artificial and human—so often mistake noise for structure. Blending clear technical insight with reflective analysis, the book reveals how models latch onto the wrong patterns, why memorization masquerades as intelligence, and what real generalization actually requires. Through case studies, cognitive parallels, and a deep look at modern foundation models, it shows that failure isn’t just error—it’s a window into the hidden assumptions shaping every mind. This is a guide to understanding how systems learn, how they mislearn, and how we can build models—and habits of thought—that see the world more clearly.

Book information

Rating

★★★★ (4.5) (2)

Genre

Technology

Length

1 hr 4 mins

Publish date

Nov 13, 2025

Language

English

About the Author

Justin Z. Martinez

Justin Z. Martinez is an storyteller, and creative technologist who explores the edges of consciousness, technology, and myth. Blending deep learning with deep meaning, his work spans from children's tales about quantum physics to futuristic sagas and spiritual reflections. He’s the creator of projects like Tiny Little Planck and The Seven Seekers. Justin’s books bridge the mystical and the modern—inviting readers to imagine, awaken, and evolve.View all Audiobooks by Justin Z. Martinez

1Chapter 1

6Chapter 6

2Chapter 2

7Chapter 7

3Chapter 3

8Chapter 8

4Chapter 4

9Chapter 9

5Chapter 5

10Chapter 10