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

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!!