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Recurrent Neural Networks (RNN)
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How do gated recurrent units (GRUs) enhance the processing of real-time data in recurrent neural networks (RNNs)?

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A

By using a single activation function for all nodes

B

By selectively forgetting irrelevant information and maintaining important features

C

By increasing the number of hidden layers in the network

D

By processing all data at once without any temporal consideration

Understanding the Answer

Let's break down why this is correct

GRUs use gating mechanisms that decide how much past information to keep and how much new input to accept. Other options are incorrect because The idea that a single activation function is used for all nodes is a misconception; Adding more hidden layers does not describe what GRUs do.

Key Concepts

gated recurrent unit (GRU)
real-time data processing
Topic

Recurrent Neural Networks (RNN)

Difficulty

medium level question

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understand

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Definition
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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