Attention Mechanisms

Attention mechanisms are techniques in machine learning that help models focus on specific parts of input data when making decisions. Instead of treating all information equally, attention allows a model to weigh different pieces based on their relevance. For example, in translating a sentence, the model can concentrate more on certain words that are crucial for understanding context, rather than processing every word the same way. This selective focus improves performance in tasks like translation, summarization, and image recognition by aligning the model’s output more closely with important input features.