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Recurrent Neural Networks (RNN)
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Given the sequence of words in a sentence, which of the following RNN variations would be best suited for understanding long-term dependencies in that sequence, especially when the context of earlier words must be maintained for later words?

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A

Long Short-Term Memory (LSTM)

B

Gated Recurrent Unit (GRU)

C

Vanilla RNN

D

Feedforward Neural Network

Understanding the Answer

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LSTM models keep a memory cell that updates slowly, so old information can still influence later steps. Other options are incorrect because GRU is a simpler version of LSTM, but it still shares the same short‑term limitation because its single gate can’t hold as much long‑term memory as LSTM’s separate gates; Vanilla RNNs compute a new hidden state by multiplying the previous state with a weight matrix.

Key Concepts

Recurrent Neural Networks
Long-term dependencies
Vanishing gradient problem
Topic

Recurrent Neural Networks (RNN)

Difficulty

hard 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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