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What makes Recurrent Neural Networks (RNNs) different from normal neural networks?
What is the role of RNNs in understanding sequences?
Which layer of an RNN is responsible for combining present input with past information?
What is the purpose of the loss function in RNNs?
Which technique is used to go back in time, understand mistakes, and update weights in RNNs?
What is the learning rate in RNNs responsible for?
Which type of RNN is like an upgraded version of the magic box in standard RNNs?
What is the purpose of regularization techniques in RNNs?
When should you use RNNs according to the article?
What is one of the limitations of RNNs mentioned in the article?
Which real-world application is mentioned as an example of using RNNs?
What is the future potential of RNNs according to the article?
What is one key point summarized in the conclusion of the article?
Which of the following is NOT a variant of RNNs?
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