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FreeMIT 6.7960 Deep Learning, Fall 2024
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This MIT OpenCourseWare offering balances theory and application as it goes through the basics of deep learning. The topics span neural architectures including MLPs, CNNs, RNNs, graph networks and transformers, plus backpropagation and automatic differentiation.
This is a better fit for learners who want the reasoning behind architectures than for those who want a quick tour of tools. The emphasis on theory means the mathematics will carry real weight. Three instructors teach it, and the playlist is a useful companion for self-study.
Instructors: Phillip Isola, Sara Beery, Dr. 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
This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.
License: Creative Commons BY-NC-SA
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