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Question & Answer
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They can maintain a memory of previous inputs
They require less data for training
They are simpler to implement than feedforward networks
They eliminate the need for any preprocessing of data
Understanding the Answer
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Recurrent Neural Networks keep a hidden state that updates with each new input. Other options are incorrect because Some think RNNs need less data, but they actually need many examples to learn the patterns in sequences; RNNs are not simpler; they have loops that feed outputs back into the network.
Key Concepts
Recurrent Neural Networks (RNN)
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Deep Dive: Recurrent Neural Networks (RNN)
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Definition
Recurrent neural networks, including LSTM and gated recurrent networks, have been widely used for sequence modeling and transduction tasks. These networks factor computation along symbol positions and generate hidden states sequentially, limiting parallelization and efficiency.
Topic Definition
Recurrent neural networks, including LSTM and gated recurrent networks, have been widely used for sequence modeling and transduction tasks. These networks factor computation along symbol positions and generate hidden states sequentially, limiting parallelization and efficiency.
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