Attention mechanism is a key component in artificial intelligence and machine learning models that allows the model to focus on specific parts of the input data while discarding irrelevant information. It was inspired by human visual attention, which enables us to selectively process and focus on certain aspects of our environment. In the context of neural networks, attention mechanisms enhance the efficiency and performance of models by assigning different weights to various parts of the input data based on their relevance to the task at hand. This allows the model to better capture long-range dependencies and relationships within the data, leading to improved accuracy and generalization. Attention mechanisms have been successfully applied in various tasks such as machine translation, image captioning, and speech recognition, among others.