MIT OpenCourseWare

Browse materialsMIT 6.7960 · Fall 2024

Deep Learning

Subject: Artificial Intelligence

Art: Palmer, Edward, 1803-1872, Figures 113-123. Plate Electrical Machines, 1840 · Public domain

Instructors: Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein
Term: Fall 2024
Level: Undergraduate, Graduate
What's inside: 24 videos · 1 solution · 1 reading · 57 other files · 4 web pages
Materials open: Videos here · files on MIT OCW

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Start course: Lec 01. Introduction to Deep Learning Find a topic

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About this course

Source: MIT OpenCourseWare 6.7960 Deep Learning, Fall 2024, Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.

Study loop: Introduction to Deep Learning

MIT marks the notation and Chapter 12 readings required and Chapter 13 optional. Homework 1 is shown as RedLectern's suggested study order.

Watch · Lec 01. Introduction to Deep Learning MIT resource
Read · Lecture 1 notes (required) · Course notation (required) · Chapter 12: Neural Networks (required) · Chapter 13: Neural Networks as Distribution Transformers (optional) MIT resource
Attempt · 6.7960 Homework 1 RedLectern's suggested study order
Continue · How to Train a Neural Net

Has MIT solutions 1 file in this course is named by MIT as solutions.

Before you start

Prerequisites are not listed here. Check MIT's own syllabus for this course.

How this course was organized

What's here, and what isn't

MIT's own materials here did not support a verified, course-wide order, so they are grouped by section instead.

Not included here

79 items from the source could not be safely included:

MIT's page format for this item wasn't recognized clearly enough to place it safely. (79 items)

Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Readings

6.7960 Math NotationPDF Download

Lecture Notes

23 files

Homework

9 files

24 video transcripts

Final Project

Blog Template (ZIP)ZIP archive Download

MIT OpenCourseWare, Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein, 6.7960 Deep Learning, Fall 2024.

License: CC BY-NC-SA 4.0 · Edition: Fall 2024 · Open the original course on OCW ↗

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Deep Learning

Study loop: Introduction to Deep Learning

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Lecture Notes

Homework

Final Project

Lec 01. Introduction to Deep Learning

Lec 02. How to Train a Neural Net

Lec 03. Approximation Theory

Lec 04. Architectures: Grids

Lec 05. Architectures: Graphs

Lec 06. Generalization Theory

Lec 07. Scaling Rules for Optimization

Lec 08. Architectures: Transformers

Lec 09. Hacker's Guide to Deep Learning

Lec 10. Architectures: Memory

Lec 11. Representation Learning: Reconstruction-Based

Lec 12. Representation Learning: Similarity-Based

Lec 13. Representation Learning: Theory

Lec 14. Generative Models: Basics

Lec 15. Generative Models: Representation Learning Meets Generative Modeling

Lec 16. Generative Models: Conditional Models

Lec 17. Generalization: Out-of-Distribution (OOD)

Lec 18. Transfer Learning: Models

Lec 19. Transfer Learning: Data

Lec 20. Scaling Laws

Lec 21. Language Models

Lec 23. Metrized Deep Learning

Lec 24. Inference Methods for Deep Learning

PyTorch Tutorial