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MIT 6.7960 Deep Learning, Fall 2024
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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 More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments .
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MIT OpenCourseWare
Subjects
fundamentals, deep learning, theory, applications, neural net architectures, MLPs, CNNs, RNNs, graph nets, transformers, geometry, invariances, backpropagation, automatic differentiation, learning theory, generalization, high dimensions, computer vision
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