?How do recurrent neural networks (RNNs) contribute to natural language processing tasks

Recurrent neural networks are well-suited for processing sequential data, making them ideal for tasks such as language modeling, text generation, and machine translation. RNNs maintain a memory of previous inputs, allowing them to capture contextual information crucial for understanding and generating natural language.

?What are the advantages of using transformer-based models like BERT in natural language processing

Transformer-based models, such as BERT (Bidirectional Encoder Representations from Transformers), have revolutionized NLP tasks by capturing bidirectional context effectively. They excel in tasks like sentiment analysis, question answering, and text classification, outperforming traditional models by leveraging large-scale pretraining on vast amounts of text data.

Deep Learning in Natural Language Processing Tutorial

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