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Question & Answer
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They maintain hidden states that capture previous information.
They can process all input data simultaneously.
They only work with numerical data.
They are less complex than feedforward neural networks.
Understanding the Answer
Let's break down why this is correct
RNNs keep a hidden state that remembers what happened before. Other options are incorrect because The idea that RNNs can look at all data at once is a misunderstanding; Thinking that RNNs only work with numbers is incorrect.
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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