LSTM (Long Short-Term Memory) and Transformer models are popular for handling sequential data in deep learning.

LSTM Pros:

Good at capturing long-range dependencies
Can handle variable-length sequences
LSTM Cons:

Sequential computation
Limited to fixed-sized context windows
Transformer Pros:

Parallelizable
Better at capturing long-range dependencies
Scalable
Transformer Cons:

Memory-intensive
May require more data
The choice between LSTMs and Transformers depends on the specific use case, data availability, and computational resources. Transformers are commonly used for natural language processing tasks due to their performance and scalability, while LSTMs can be suitable for specific applications or limited data/resource situations.
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