MIT 6.7960 Deep Learning, Fall 2024

Total watch time

1d 5h 32m

at 1× speed · exactly 1 day, 5 hours, 31 minutes, 50 seconds at 1×

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1×	1d 5h 31m 50s
1.25×	23h 37m 28s
1.5×	19h 41m 13s
1.75×	16h 52m 29s
2×	14h 45m 55s

Average video1h 13m 50s

Longest1h 25m 42s

Shortest29m

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Plan to finish

About 30 days at an hour a day

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At one hour a day, every day, this playlist takes about 30 days. Turn on JavaScript to plan around your week.

Videos (24)

1d 5h 32m in total · tick what you've watched

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1	Lec 01. Introduction to Deep Learning	1:00:51	2026-02-11
2	Lec 02. How to Train a Neural Net	1:19:34	2026-02-11
3	Lec 03. Approximation Theory	1:22:42	2026-02-11
4	Lec 04. Architectures: Grids	1:23:57	2026-02-11
5	Lec 05. Architectures: Graphs	1:21:14	2026-02-11
6	Lec 06. Generalization Theory	1:20:31	2026-02-11
7	Lec 07. Scaling Rules for Optimization	1:20:56	2026-02-11
8	Lec 08. Architectures: Transformers	1:14:35	2026-02-11
9	Lec 09. Hacker's Guide to Deep Learning	1:15:51	2026-02-11
10	Lec 10. Architectures: Memory	1:13:28	2026-02-11
11	Lec 11. Representation Learning: Reconstruction-Based	1:21:04	2026-02-11
12	Lec 12. Representation Learning: Similarity-Based	1:16:20	2026-02-11
13	Lec 13. Representation Learning: Theory	1:15:21	2026-02-11
14	Lec 14. Generative Models: Basics	1:21:18	2026-02-11
15	Lec 15. Generative Models: Representation Learning Meets Generative Modeling	1:20:40	2026-02-11
16	Lec 16. Generative Models: Conditional Models	1:21:32	2026-02-11
17	Lec 17. Generalization: Out-of-Distribution (OOD)	1:04:41	2026-02-11
18	Lec 18. Transfer Learning: Models	1:25:42	2026-02-11
19	Lec 19. Transfer Learning: Data	1:15:44	2026-02-11
20	Lec 20. Scaling Laws	38:23	2026-02-11
21	Lec 21. Language Models	1:17:23	2026-02-11
22	Lec 23. Metrized Deep Learning	1:07:50	2026-02-11
23	Lec 24. Inference Methods for Deep Learning	1:23:13	2026-02-11
24	PyTorch Tutorial	29:00	2026-02-11

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Data from the YouTube Data API as of 2026-09-22 (cached). Private or deleted videos are excluded from totals.