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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