Explore the fundamentals of Recurrent Neural Networks in this 45-minute lecture from MIT's Introduction to Deep Learning course. Delve into sequence modeling, RNN architecture, and intuition behind these powerful models. Learn about unfolding RNNs, backpropagation through time, and how to address gradient issues. Discover the Long Short-Term Memory (LSTM) architecture and its advantages. Examine various RNN applications and get introduced to the concept of attention in neural networks. Gain a comprehensive understanding of these essential deep learning concepts through clear explanations and practical examples.
MIT 6.S191 - Recurrent Neural Networks
Alexander Amini and Massachusetts Institute of Technology via YouTube
Overview
Syllabus
- Introduction
- Sequence modeling
- Recurrent neural networks
- RNN intuition
- Unfolding RNNs
- Backpropagation through time
- Gradient issues
- Long short term memory LSTM
- RNN applications
- Attention
- Summary
Taught by
https://www.youtube.com/@AAmini/videos
Tags
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