Search

11: Generative AI – Text-to-Image Models

10: Generative AI – Adapting LLMs with Parameter-Efficient Fine-Tuning

9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)

8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning

7: Deep Learning for Natural Language – Transformers

6: Deep Learning for Natural Language – Embeddings

5: Deep Learning for Natural Language – The Basics

4: Deep Learning for Computer Vision – Transfer Learning and Fine-Tuning; Intro to HuggingFace

3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch

2: Training Deep NNs (cont.); Introduction to Keras/Tensorflow; Application to Tabular Data

1: Introduction to Neural Networks and Deep Learning; Training Deep NNs

MIT 15.773 Hands-On Deep Learning Spring 2024

Aaron Bateman. "Weapons in Space: Technology, Politics, and the Rise and Fall of the Strategic Defense Initiative" (MIT Press, 2024)

AI and Our Time at Work: Featuring MIT Neil Thompson and David Autor

Dagmar Schafer, "Ownership of Knowledge: Beyond Intellectual Property" (MIT Press, 2023)

Sharon Sliwinski, "An Alphabet for Dreamers: How to See the World with Eyes Closed" (MIT Press, 2025)

Enterprise IT Operating Models in the AI Era

Why So Much Food is Lost: Inside Global Food Supply Chains

Lecture 20: Reed-Solomon Codes

Lecture 19: Error-Correcting Codes—Hamming Codes