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Recurrent Neural Networks (RNN)
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In Recurrent Neural Networks, the primary issue that limits their efficiency and parallelization during training is the sequential generation of ____, which impacts how information flows through the network.

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A

hidden states

B

input sequences

C

activation functions

D

output layers

Understanding the Answer

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Each hidden state depends on the previous hidden state. Other options are incorrect because Input sequences are the data fed into the network, but the network can still process them in parallel; Activation functions are applied element‑wise and can be calculated in parallel.

Key Concepts

Recurrent Neural Networks
Hidden States
Sequence Modeling
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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