MIT Introduction to Deep Learning 6.S191: Lecture 1
New 2023 Edition
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: http://introtodeeplearning.com/
Lecture Outline
0:00 - Introduction
8:14 - Course information
11:33 - Why deep learning?
14:48 - The perceptron
20:06 - Perceptron example
23:14 - From perceptrons to neural networks
29:34 - Applying neural networks
32:29 - Loss functions
35:12 - Training and gradient descent
40:25 - Backpropagation
44:05 - Setting the learning rate
48:09 - Batched gradient descent
51:25 - Regularization: dropout and early stopping
57:16 - Summary