Uploaded March 2022 | 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
6:35 - Course information
9:51 - Why deep learning?
12:30 - The perceptron
14:31 - Activation functions
17:03 - Perceptron example
20:25 - From perceptrons to neural networks
26:37 - Applying neural networks
29:18 - Loss functions
31:19 - Training and gradient descent
35:46 - Backpropagation
38:55 - Setting the learning rate
41:37 - Batched gradient descent
43:45 - Regularization: dropout and early stopping
47:58 - 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
6:35 - Course information
9:51 - Why deep learning?
12:30 - The perceptron
14:31 - Activation functions
17:03 - Perceptron example
20:25 - From perceptrons to neural networks
26:37 - Applying neural networks
29:18 - Loss functions
31:19 - Training and gradient descent
35:46 - Backpropagation
38:55 - Setting the learning rate
41:37 - Batched gradient descent
43:45 - Regularization: dropout and early stopping
47:58 - 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!!
