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

MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Amini ** New 2024 Edition ** For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00​ - Introduction 3:42​ - Sequence modeling 5:30​ - Neurons with recurrence 12:20 - Recurrent neural networks 14:08 - RNN intuition 17:14​ - Unfolding RNNs 19:54 - RNNs from scratch 22:41 - Design criteria for sequential modeling 24:24 - Word prediction example 31:50​ - Backpropagation through time 33:40 - Gradient issues 37:15​ - Long short term memory (LSTM) 40:00​ - RNN applications 44:00- Attention fundamentals 46:46 - Intuition of attention 49:13 - Attention and search relationship 51:22 - Learning attention with neural networks 57:45 - Scaling attention and applications 1:00:08 - 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!!