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1
​ - Introduction
2
​ - Sequence modeling
3
​ - Neurons with recurrence
4
- Recurrent neural networks
5
- RNN intuition
6
​ - Unfolding RNNs
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- RNNs from scratch
8
- Design criteria for sequential modeling
9
- Word prediction example
10
​ - Backpropagation through time
11
- Gradient issues
12
​ - Long short term memory LSTM
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​ - RNN applications
14
- Attention fundamentals
15
- Intuition of attention
16
- Attention and search relationship
17
- Learning attention with neural networks
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- Scaling attention and applications
19
- Summary
Description:
Dive into the world of advanced deep learning techniques with this comprehensive lecture from MIT's Introduction to Deep Learning course. Explore the intricacies of Recurrent Neural Networks (RNNs), Transformers, and Attention mechanisms. Begin with an introduction to sequence modeling and neurons with recurrence, then delve into the fundamentals of RNNs, including their intuition and unfolding process. Learn how to build RNNs from scratch and understand the design criteria for sequential modeling through a word prediction example. Discover the backpropagation through time algorithm and address gradient issues in RNNs. Investigate Long Short-Term Memory (LSTM) networks and their applications. Finally, uncover the power of attention mechanisms, their intuition, relationship to search, and implementation in neural networks. Gain insights into scaling attention and its various applications in deep learning.

Recurrent Neural Networks, Transformers, and Attention

Alexander Amini
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#Computer Science #Machine Learning #Sequence Modeling #Deep Learning #Artificial Intelligence #Neural Networks #Recurrent Neural Networks (RNN) #Long short-term memory (LSTM) #Attention Mechanisms #Backpropagation
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​ - Introduction