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conditioning

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Generative_AI

This repository is a comprehensive resource for mastering generative AI, featuring in-depth notes and exciting projects. The goal is to stay updated with the latest advancements in generative AI, and explore applications in image & video generation, creative content creation. Explore the limitless possibilities of generative AI today!

  • Updated Jan 3, 2025
  • Jupyter Notebook

Least-squares regression implemented three ways — normal equations, QR decomposition, and gradient descent — with an empirical study of numerical conditioning. Demonstrates ~7 orders of magnitude accuracy loss in the textbook formula on ill-conditioned data.

  • Updated Sep 6, 2026
  • Python

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