MIT Introduction to Deep Learning | 6.S191

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