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

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Lecture Notes

Lecture 1: Introduction to Deep Learning

Lecture 2: How to Train a Neural Net

Lecture 3: Approximation Theory

Lecture 4: Architectures for Grids

Lecture 5: Graph Neural Networks

Lecture 6: Neural Network Generalization

Lecture 7: Scaling Rules for Optimization

Lecture 8: Transformers

Lecture 9: Hacker's Guide to Deep Learning

Lecture 10: Memory and Sequence Modeling

Lecture 11: Representation Learning I

Lecture 12: Similarity-Based Representation Learning

Lecture 13: Architectural Bias on Representations

Lecture 14: Deep Generative Models I

Lecture 15: Deep Generative Models II

Lecture 16: Deep Generative Models III

Lecture 17: Out-of-Distribution Generalization

Lecture 18: Transfer Learning I

Lecture 19: Transfer Learning II

Lecture 20: Scaling Laws

Lecture 21: Language Models

Lecture 23: Metrized Deep Learning

Lecture 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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