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MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention

MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Amini 2023 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00​ - Introduction 3:07​ - Sequence modeling 5:09​ - Neurons with recurrence 12:05 - Recurrent neural networks 13:47 - RNN intuition 15:03​ - Unfolding RNNs 18:57 - RNNs from scratch 21:50 - Design criteria for sequential modeling 23:45 - Word prediction example 29:57​ - Backpropagation through time 32:25 - Gradient issues 37:03​ - Long short term memory (LSTM) 39:50​ - RNN applications 44:50 - Attention fundamentals 48:10 - Intuition of attention 50:30 - Attention and search relationship 52:40 - Learning attention with neural networks 58:16 - Scaling attention and applications 1:02:02 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!