Deep Learning
6.7960 · Electrical Engineering and Computer Science · Undergraduate, Graduate · Fall 2024
Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein
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.
The syllabus, on MIT OpenCourseWare
The full course — syllabus, assigned readings, problem sets, exams, and lecture notes — lives on OCW. These open the real thing:
Syllabus ↗
Course overview, grading, schedule
Readings ↗
The assigned reading list, session by session
Assignments ↗
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Attribution
Prof. Phillip Isola, Prof. Sara Beery, Dr. Jeremy Bernstein. 6.7960 Deep Learning. Fall 2024. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: CC BY-NC-SA 4.0.
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