- Published on December 4, 2021
- In Deep Tech
Attention Layer – Examples and Implementation
In many of the cases, we see that the traditional neural networks are not capable of holding and working on long and large information. attention layer can help a neural network in memorizing the large sequences of data.
- By Yugesh Verma
If we are providing a huge dataset to the model to learn, it is possible that a few important parts of the data might be ignored by the models. Paying attention to important information is necessary and it can improve the performance of the model. This can be achieved by adding an additional attention feature to the models. Neural networks built using different layers can easily incorporate this feature through one of the layers. We can use the attention layer in its architecture to improve its performance. In this article, we are going to discuss the attention layer in neural networks and we understand its significance and how it can be added to the network practically. The major points that we will discuss here are listed below.
Table of contentsProblem with Neural NetworksWhat is an
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Yugesh Verma
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.
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