MIT Introduction to Deep Learning | 6.S191

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2023 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

Lecture Outline
0:00​ – Introduction
8:14 ​ – Course information
11:33​ – Why deep learning?
14:48​ – The perceptron
20:06​ – Perceptron example
23:14​ – From perceptrons to neural networks
29:34​ – Applying neural networks
32:29​ – Loss functions
35:12​ – Training and gradient descent
40:25​ – Backpropagation
44:05​ – Setting the learning rate
48:09​ – Batched gradient descent
51:25​ – Regularization: dropout and early stopping
57:16​ – Summary

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