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MIT 6.S191 - Recurrent Neural Networks

Alexander Amini and Massachusetts Institute of Technology via YouTube

Overview

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This lecture introduces recurrent neural networks for modeling sequential data. It covers sequence representations, RNN architecture and training with backpropagation through time, gradient issues, LSTMs, attention, and applications including language modeling.

Syllabus

​ - Introduction
​ - Sequence modeling
​ - Neurons with recurrence
​ - Recurrent neural networks
​ - RNN intuition
​ - Unfolding RNNs
- RNNs from scratch
- Design criteria for sequential modelling
- Word prediction example
​ - Backpropagation through time
​ - Gradient issues
​ - Long short term memory LSTM
​ - RNN applications
​ - Attention
​ - Summary

Taught by

https://www.youtube.com/@AAmini/videos

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