Neural Networks and Fuzzy Systems
Transformers are a type of neural network architecture that leverage self-attention mechanisms to process sequential data, allowing them to effectively capture dependencies in long-range contexts. This architecture has gained popularity due to its ability to parallelize training, making it efficient for handling large datasets. Transformers have fundamentally changed how we approach tasks in natural language processing and other domains, paving the way for more complex models and innovations.
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