MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with project proposal competition with feedback from staff and panel of industry sponsors.
Prerequisites assume elementary calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!
Mon Jan 27 - Fri Jan 31, 2020
1:00pm-4:00pm, MIT Room 32-123
1:00pm-1:45pm: Lecture Part 1
1:45am-2:30pm: Lecture Part 2
2:30pm-2:40pm: Snack Break
2:40pm-4:00pm: Lab (Hands-On TensorFlow Labs)
Exact times to be confirmed for 2020.
We were recently featured on MIT News and EECS News!
3 units P/D/F based on completion of project proposal assignment. Listeners also welcome!
Reach out to firstname.lastname@example.org. To view archived versions of this website from past years please click here for 2019, 2018, and 2017.
Registration opens on Dec 3, 2019 at 9am. If you are an MIT student (undergraduate or graduate) please register here. You can specify if you want to take the course for credit or as a listener there. If you would like to receive course related updates and lecture materials please sign up for our mailing list.
All course materials are copyrighted. If you are an instructor and would like to use any materials from this course (slides, labs, code), you must add the following reference to each slide:
© MIT 6.S191: Introduction to Deep Learning