👉 Attention computing is a machine learning paradigm that enables models to focus on specific parts of input data when making predictions or generating outputs, rather than treating all inputs equally. This is particularly useful in tasks like natural language processing, where a model might need to weigh the importance of different words in a sentence when generating a response or understanding its meaning. Attention mechanisms allow the model to dynamically allocate computational resources to relevant parts of the input, improving accuracy and efficiency. By mimicking human attention, where we selectively focus on certain stimuli while ignoring others, attention computing enhances the model's ability to handle complex and context-dependent tasks.
