Transformers are a type of neural network architecture introduced in 2017, designed to handle sequential data by capturing relationships between elements in a sequence.
Unlike traditional models that process data in a fixed order, transformers utilize a mechanism called attention to weigh the influence of different elements, allowing for more flexible and efficient processing.
Key Features:
- Attention Mechanism: Enables the model to focus on relevant parts of the input sequence, effectively capturing dependencies regardless of their distance in the sequence.
- Parallel Processing: Allows for simultaneous computation of sequence elements, leading to faster training times compared to recurrent models.
Applications:
- Natural Language Processing (NLP): Transformers have become foundational in tasks like machine translation, text summarization, and sentiment analysis due to their ability to understand context.
- Computer Vision: Adapted for image classification and object detection, transformers capture spatial relationships within images effectively.
- Speech Processing: Applied in speech recognition and synthesis, transformers model temporal dependencies in audio data.