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Recurrent Neural Networks (RNN)
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In what scenario would using an RNN (like LSTM) be more advantageous than a traditional feedforward neural network?

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Choose the Best Answer

A

When processing sequential data such as time series or text

B

When working with static images

C

When the dataset is small and simple

D

When speed of training is the primary concern

Understanding the Answer

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Answer

RNNs are useful when the data have a temporal or sequential structure, such as sentences, time‑series, or audio. They keep a hidden state that remembers past inputs, so each prediction can depend on everything that came before. For example, in language modeling the probability of the next word depends on all previous words, which a feedforward network would treat as independent. An LSTM can store long‑term dependencies like the subject of a sentence, allowing it to predict a verb correctly even when many words intervene. Thus, when past context influences the output, an RNN like LSTM outperforms a simple feedforward network.

Detailed Explanation

An RNN can remember earlier parts of a sequence because it keeps a hidden state that updates with each new input. Other options are incorrect because The mistake is thinking that images need sequence memory; The misconception is that a small, simple dataset automatically calls for an RNN.

Key Concepts

Recurrent Neural Networks (RNN)
Sequence Modeling
Feedforward Neural Networks
Topic

Recurrent Neural Networks (RNN)

Difficulty

medium level question

Cognitive Level

understand

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