Gradients: Motion and Learning

How does a machine actually "learn"? Before we can build complex simulations of planets or analyze thermodynamic systems, we need to understand the fundamental physics of a single guess. 

In this lecture, we'll start from first principles, using the simple, intuitive game of "Hot or Cold" to build a physical model for gradient descent. We'll explore how changing a single parameter—the "learning rate"—can mean the difference between finding a solution and getting stuck in a chaotic loop.

This is the first step in our journey to derive deep learning from the ground up, using the language of physics, not just computer science.

In the next video: We'll take these 1D principles and expand them into a 2D universe, using orbital mechanics to build a creative drawing tool from scratch. 

Eventually we will discuss the physical intuition behind motion, entropy and thermodynamics. Feel free to look ahead, where we discuss more physics explanations of the gradient descent algorithm that provided the basis for providing an ‘energy landscape’ in learning models, in the post “Hooks Law and Perceptrons” This will be the underlying basis for the next lecture.

https://www.djhoxie.net/samplelectures/blog-hooks-law-and-learning

Connect & Support

• Website: http://www.djhoxie.net

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AI Collaboration Note: This video, its title, description, and the concepts explored within were developed in a deep collaboration with Google Gemini. Gemini's role included acting as a Socratic partner, a reviewer, and a tool for structuring and refining the final presentation.

Acknowledgments: Thank you to Daniel Shiffman (The Coding Train) for providing excellent conceptual starting points for the p5.js demonstrations.

References: 

[1] Imran, Muhammad, and Norah Almusharraf. "Google Gemini as a next generation AI educational tool: a review of emerging educational technology." Smart Learning Environments 11, no. 1 (2024): 22. 

[2] Marquardt, F., and Marquardt, F., 2021, "Machine learning and quantum devices," SciPost Physics Lecture Notes, p. 29. 

[3] Shannon, C. E., 1948, "A mathematical theory of communication," The Bell system technical journal, 27(3), pp. 379-423.

[4] Shiffman, D. (2024). The nature of code: simulating natural systems with javascript. No Starch Press.

[5] Landauer, Rolf. "Information is physical." Physics Today 44, no. 5 (1991): 23-29.