The Attention Mechanism is a technique used in machine learning, particularly in natural language processing and computer vision. It allows models to focus on specific parts of the input data when making predictions, rather than processing all information equally. This selective focus helps improve the performance of models, especially in tasks like translation and image captioning. In the context of neural networks, the attention mechanism assigns different weights to different input elements, enabling the model to prioritize relevant information. This approach is a key component of architectures like Transformers, which have revolutionized tasks such as language translation and text generation.