MIT's introductory course on deep learning methods with applications to machine translation, image recognition, game playing, 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 proposals with feedback from staff and panel of industry sponsors.
Prerequisites assume elementary calculus (i.e. taking derivatives and applying the chain rule) 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!
This version of the course is outdated, please refer to our homepage for an updated version.
Mon Jan 28 - Fri Feb 1
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)
We were recently featured on MIT EECS News! Checkout the press release here.
3 units P/D/F based on completion of project proposal assignment. Listeners also welcome!
Reach out to firstname.lastname@example.org.
If you are an MIT student (undergraduate or graduate) please register here. You can do this by clicking "create new form" and selecting "Add Drop". Enter the subject information (6.S191) and 3 units when prompted. You can also specify if you want to be registered as a listener or regular student there.
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