What are transformers in deep learning?
Asked on Aug 28, 2025
Answer
Transformers are a type of neural network architecture that have revolutionized natural language processing by enabling models to handle sequential data more effectively through self-attention mechanisms.
Example Concept: Transformers utilize a self-attention mechanism that allows the model to weigh the importance of different words in a sentence, regardless of their position. This is achieved through multiple layers of attention and feed-forward networks, enabling the model to capture complex dependencies and relationships in data. Transformers are the foundation of many state-of-the-art models like BERT and GPT.
Additional Comment:
- Transformers do not rely on recurrent layers, which makes them more efficient for parallel processing.
- The self-attention mechanism helps in understanding context by focusing on relevant parts of the input sequence.
- Transformers have been widely adopted in NLP tasks such as translation, summarization, and question answering.
- They have also been adapted for use in other domains like image processing and protein folding.
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