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!
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!
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© MIT 6.S191: Introduction to Deep Learning