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
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!!