MIT Introduction to Deep Learning 6.S191: Lecture 1 *New 2020 Edition*

MIT Introduction to Deep Learning 6.S191: Lecture 1 *New 2020 Edition*


https://www.youtube.com/watch?v=njKP3FqW3Sk&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&index=2

MIT Introduction to Deep Learning 6.S191: Lecture 1 *New 2020 Edition* Foundations of Deep Learning Lecturer: Alexander Amini January 2020 For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 - Introduction 4:14 - Course information 8:10 - Why deep learning? 11:01 - The perceptron 13:07 - Activation functions 15:32 - Perceptron example 18:54 - From perceptrons to neural networks 25:23 - Applying neural networks 28:16 - Loss functions 31:14 - Training and gradient descent 35:13 - Backpropagation 39:25 - Setting the learning rate 43:43 - Batched gradient descent 46:46 - Regularization: dropout and early stopping 51:58 - 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!!