Uploaded February 2021 | Updated September 2026, 2 weeks ago
MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: introtodeeplearning.com
Lecture Outline
0:00 - Introduction
4:48 - Course information
10:18 - Why deep learning?
12:28 - The perceptron
14:42 - Activation functions
17:48 - Perceptron example
21:43 - From perceptrons to neural networks
27:42 - Applying neural networks
30:21 - Loss functions
33:23 - Training and gradient descent
38:05 - Backpropagation
43:06 - Setting the learning rate
47:17 - Batched gradient descent
49:49 - Regularization: dropout and early stopping
55:55 - Summary
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MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: introtodeeplearning.com
Lecture Outline
0:00 - Introduction
4:48 - Course information
10:18 - Why deep learning?
12:28 - The perceptron
14:42 - Activation functions
17:48 - Perceptron example
21:43 - From perceptrons to neural networks
27:42 - Applying neural networks
30:21 - Loss functions
33:23 - Training and gradient descent
38:05 - Backpropagation
43:06 - Setting the learning rate
47:17 - Batched gradient descent
49:49 - Regularization: dropout and early stopping
55:55 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
