MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
2023 Edition

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

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fullyconnected!!