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About Attention and Memory in Deep Learning and NLP
This document discusses attention mechanisms in deep learning and natural language processing (NLP). It explains that attention mechanisms allow neural networks to focus on specific parts of input data, like words in a sentence, when generating output. Attention was originally applied to machine translation but can be used in any recurrent neural network model, such as image captioning or text comprehension. The document provides examples of how attention allows models to focus on different parts of input over time, which can be visualized and improves performance compared to encoding all input into a fixed vector. However, attention comes at a computational cost compared to human attention.
Detailed Information
| Author: | ['OnixHoque'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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