MIT Introduction to Deep Learning 6.S191: Lecture 1 *New 2021 Edition* Foundations of Deep Learning Lecturer: Alexander Amini For all lectures, slides, and lab materials: http://introtodeeplearning.com/ Lecture Outline 0:00​ - Introduction 4:48 ​ - Course information 10:18​ - Why deep learning? 12:28​ - The perceptron 14:42​ - Activation functions 17:48​ - Perceptron example 21:43​ - From perceptrons to neural networks 27:42​ - Applying neural networks 30:21​ - Loss functions 33:23​ - Training and gradient descent 38:05​ - Backpropagation 43:06​ - Setting the learning rate 47:17​ - Batched gradient descent 49:49​ - Regularization: dropout and early stopping 55:55​ - 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!!