MIT Introduction to Deep Learning 6.S191: Lecture 1
New 2024 Edition
Foundations of Deep Learning
Lecturer: Alexander Amini

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

Lecture Outline
0:00​ Introduction
7:25​ Course information
13:37​ Why deep learning?
17:20​ The perceptron
24:30​ Perceptron example
31;16​ From perceptrons to neural networks
37:51​ Applying neural networks
41:12​ Loss functions
44:22​ Training and gradient descent
49:52​ Backpropagation
54:57​ Setting the learning rate
58:54​ Batched gradient descent
1:02:28​ Regularization: dropout and early stopping
1:08:47 Summary

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