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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 03: Approximation Theory (6.7960)

Jeremy BernsteinMIT

Professor

1:22:42·2026·graduate level

This lecture investigates the approximation capabilities of deep neural networks, covering universal approximation theorems and Barron's theorem. It also discusses the impact of network depth on expressivity.

AI & MLDeep LearningApproximation TheoryBarron's TheoremNeural Network Expressivity

MIT 6.7960 Deep Learning, Fall 2024

Instructor: Jeremy Bernstein

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

Previous · Lecture 2

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

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Lecture 04: Architectures: Grids (6.7960)

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