Self-Driving Cars
Machine Learning
A neural network mechanism that allows models to weigh the importance of different parts of the input sequence dynamically. This is a key component of transformer architectures.
Detailed Explanation
Self-Attention is a mechanism in neural networks, particularly in transformers, that enables the model to evaluate and assign different levels of importance to each element within an input sequence. By dynamically adjusting weights, it helps the model capture contextual relationships and dependencies across words or tokens, improving understanding of long-range connections in tasks like language translation, summarization, and question-answering.
Use Cases
- •Enhances language translation accuracy by dynamically focusing on relevant words, capturing contextual relationships across entire sentences.
Related Terms
Other terms in the Machine Learning category