This course on Recurrent Neural Networks aims to teach students the fundamentals of sequence modeling, neurons with recurrence, RNN intuition, LSTM, and attention mechanisms. Students will learn about designing criteria for sequential modeling, word prediction, backpropagation through time, and common gradient issues. The teaching method includes lectures with slides and lab materials. This course is intended for individuals interested in deep learning and neural networks, particularly those looking to understand and apply RNNs in various applications.
MIT 6.S191 - Recurrent Neural Networks
Alexander Amini and Massachusetts Institute of Technology via YouTube
Overview
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
