Deep Learning | Electrical Engineering and Computer Science | MIT OpenCourseWare

Course Info

Instructors

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2024

Level

Undergraduate
Graduate

Topics

Engineering
- Computer Science
  - Artificial Intelligence
  - Graphics and Visualization

Learning Resource Types

Lecture Notes

Lecture Videos

Problem Sets

Projects with Examples

Readings

Download Course

Lecture Videos

PyTorch Tutorial

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

Course Info

Instructors

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2024

Level

Undergraduate
Graduate

Topics

Engineering
- Computer Science
  - Artificial Intelligence
  - Graphics and Visualization

Learning Resource Types

Lecture Notes

Lecture Videos

Problem Sets

Projects with Examples

Readings

Download Course

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