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MIT 6.7960: Deep Learning →0/20
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Lecture 01: Introduction to Deep Learning (6.7960)

1:00:51
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Lecture 02: How to Train a Neural Net (6.7960)

1:19:34
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Lecture 03: Approximation Theory (6.7960)

1:22:42
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Lecture 04: Architectures: Grids (6.7960)

1:23:57
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Lecture 05: Architectures: Graphs (6.7960)

1:21:14
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Lecture 06: Generalization Theory (6.7960)

1:20:31
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Lecture 07: Scaling Rules for Optimization (6.7960)

1:20:56
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Lecture 08: Architectures: Transformers (6.7960)

1:14:35
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Lecture 09: Hacker's Guide to Deep Learning (6.7960)

1:15:51
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Lecture 10: Architectures: Memory (6.7960)

1:13:28
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Lecture 11: Representation Learning: Reconstruction-Based (6.7960)

1:21:04
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Lecture 12: Representation Learning: Similarity-Based (6.7960)

1:16:20
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Lecture 13: Representation Learning: Theory (6.7960)

1:15:21
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Lecture 14: Generative Models: Basics (6.7960)

1:21:18
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Lecture 15: Generative Models: Representation Learning Meets Generative Modeling (6.7960)

1:20:40
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Lecture 16: Generative Models: Conditional Models (6.7960)

1:21:32
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Lecture 17: Generalization: Out-of-Distribution (OOD) (6.7960)

1:04:41
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Lecture 18: Transfer Learning: Models (6.7960)

1:25:42
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Lecture 19: Transfer Learning: Data (6.7960)

1:15:44
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Lecture 20: Scaling Laws (6.7960)

38:23

Lecture 05: Architectures: Graphs (6.7960)

Phillip IsolaMIT

Professor

1:21:14·2026·graduate level

This session covers graph neural networks (GNNs), demonstrating their connections to MLPs and CNNs, and detailing message passing algorithms. Theoretical limitations of GNN expressive power are also discussed.

Theory & AlgosAI & MLDeep LearningAlgorithms & Data StructuresGraph Neural NetworksMessage Passing Algorithms

MIT 6.7960 Deep Learning, Fall 2024

Instructor: Phillip Isola

View the complete course: https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/

YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8

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Lecture 04: Architectures: Grids (6.7960)

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Lecture 06: Generalization Theory (6.7960)

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