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
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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