MIT Introduction to Deep Learning 6S191

Learners: 964,544

Instructor: Alexander Amini

Duration:

MIT's Introduction to Deep Learning 6.S191 is a comprehensive course for those interested in learning the foundations of deep learning. Led by Alexander Amini, the course covers topics such as the perceptron, neural networks, loss functions, training and gradient descent, backpropagation, setting the learning rate, batched gradient descent, and regularization. With slides and lab materials available at http://introtodeeplearning.com/, this course is perfect for those looking to gain a better understanding of deep learning. Subscribe to stay up to date with new lectures or follow @MITDeepLearning on Twitter and Instagram to stay connected! ▼▲

Course Feature Course Overview Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Free

Provider:

Youtube

Certificate:

No Information

Language:

English

Course Overview

❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

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.