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RNNs process input sequences one element at a time, maintaining a hidden state that captures information from previous inputs.
RNNs can analyze all input data simultaneously, making them faster than traditional neural networks.
RNNs only work with numerical data, making them unsuitable for textual conversations.
RNNs are less complex than feedforward neural networks, simplifying the design process.
Understanding the Answer
Let's break down why this is correct
RNNs read a sequence one step at a time and keep a hidden state that remembers what came before. Other options are incorrect because The idea that RNNs process all data at once is wrong; RNNs can work with text, but they need text turned into numbers first.
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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