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Updated in [July 21st, 2023]

MIT 6.S191: Introduction to Deep Learning is a course designed to provide an overview of the foundations of deep learning. Led by Alexander Amini, the course will cover topics such as the perceptron, neural networks, loss functions, training and gradient descent, backpropagation, setting the learning rate, batched gradient descent, and regularization. Through lectures, slides, and lab materials, students will gain a comprehensive understanding of the fundamentals of deep learning.

The course will begin with an introduction and course information, followed by an exploration of why deep learning is important. Students will then learn about the perceptron and how it works, as well as how to apply neural networks. Loss functions, training and gradient descent, and backpropagation will also be discussed. The course will conclude with a discussion of setting the learning rate, batched gradient descent, and regularization.

By the end of the course, students will have a comprehensive understanding of the fundamentals of deep learning. Those interested in staying up to date with new deep learning lectures at MIT can subscribe, or follow @MITDeepLearning on Twitter and Instagram to stay fully-connected.