MIT Introduction to Deep Learning | 6.S191

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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