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 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!!