My markdown notes and Jupyter notebooks for the efficientML.ai 2026 course videos from MIT's HAN Lab, with explanations and implementation details. The course explores how to make machine learning more efficient by reducing computational costs and improving hardware utilization.

Lecture Videos	Notes
	• 01 - Introduction
	• 02 - NN Basics
	• 03 - Pruning & Sparsity I
	• 04 - Pruning & Sparsity II
	• 05 - Quantization I
	• 06 - Quantization II
	• 07 - Neural Architecture Search I
	• 08 - Neural Architecture Search II
More coming soon

The notes can be viewed directly on GitHub, no special setup required.
I recommend using Obsidian for best offline experience.

Acknowledgments

Many thanks to Professor Song Han and the MIT HAN Lab team for developing and sharing the efficientML.ai course and its educational materials. For the original course content and additional resources, visit the official course website.

Disclaimer

This repository contains personal study notes and supplementary explanations. It is an independent educational resource and is not in any way affiliated with, endorsed by, or an official publication of MIT, the MIT HAN Lab, or the efficientML.ai course staff. All original course materials remain the property of their respective authors and rights holders. Sources are linked and referenced in the notes.

I hope these notes help you as much as they helped me.