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MIT Introduction to Deep Learning | 6.S191

MIT Introduction to Deep Learning 6.S191: Lecture 1 New 2024 Edition Foundations of Deep Learning Lecturer: Alexander Amini For all lectures, slides, and lab materials: http://introtodeeplearning.com/ Lecture Outline 0:00​ - Introduction 7:25​ - Course information 13:37​ - Why deep learning? 17:20​ - The perceptron 24:30​ - Perceptron example 31;16​ - From perceptrons to neural networks 37:51​ - Applying neural networks 41:12​ - Loss functions 44:22​ - Training and gradient descent 49:52​ - Backpropagation 54:57​ - Setting the learning rate 58:54​ - Batched gradient descent 1:02:28​ - Regularization: dropout and early stopping 1:08:47 - 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!!