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 fully-connected!!
Add comment