How can you manage long-term dependencies in recurrent neural networks?
Recurrent neural networks (RNNs) are powerful machine learning models that can process sequential data, such as text, speech, or video. However, they often struggle to capture long-term dependencies, which are the relationships between distant elements in the sequence. For example, if you want to predict the next word in a sentence, you need to remember the context of the previous words, not just the last one. In this article, you will learn how to manage long-term dependencies in RNNs using different techniques and architectures.
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