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

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

Lecture Outline
0:00​ - Introduction
4:48 ​ - Course information
10:18​ - Why deep learning?
12:28​ - The perceptron
14:42​ - Activation functions
17:48​ - Perceptron example
21:43​ - From perceptrons to neural networks
27:42​ - Applying neural networks
30:21​ - Loss functions
33:23​ - Training and gradient descent
38:05​ - Backpropagation
43:06​ - Setting the learning rate
47:17​ - Batched gradient descent
49:49​ - Regularization: dropout and early stopping
55:55​ - Summary

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