Lecture 13 of 24Lecture Videos
Lec 13. Representation Learning: Theory
Prof. Phillip Isola, Prof. Sara Beery and Dr. Jeremy Bernstein · Massachusetts Institute of Technology
Credits and licence
- From
- Deep Learning, Massachusetts Institute of Technology (at MIT OpenCourseWare)
- Licence
- CC BY 4.0, as MIT OpenCourseWare states it.
- Attribution
- Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein. Deep Learning. Fall 2024. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- 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
- PyTorch Tutorial