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Lecture 12 - Introduction to Deep Learning

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2022 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://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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