MIT 6.S191: Introduction to Deep Learning - MIT Introduction to Deep Learning (2022) | 6.S191

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!!