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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