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FreeMIT Introduction to Deep Learning (6.S191)
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Built as a fast, high-intensity introduction, this program covers deep learning methods applied to language, vision, biology and other fields. It finishes with a project proposal competition that receives feedback from staff and a panel of industry sponsors.
The prerequisites are modest: comfort with derivatives and matrix multiplication, plus Python if you have it. Other concepts get explained as they arrive, which suits learners from outside computer science. Because the program is short and intense, it rewards learners who can give it focused time.
An efficient and high-intensity bootcamp designed to teach you the fundamentals of deep learning as quickly as possible!
MIT's introductory program on deep learning methods with applications to natural language processing, computer vision, biology, and more! Students will gain foundational knowledge of deep learning algorithms, practical experience in building neural networks, and understanding of cutting-edge topics including large language models and generative AI. Program concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume 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!
What pre-requisites are required?
We are expecting very elementary knowledge of linear algebra and calculus. How to multiply matrices, take derivatives and apply the chain rule. Familiarity in Python is a big plus as well.
The program will be beginner friendly since we have many registered students from outside of computer science.
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