Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose 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
Let's break down why this is correct
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
Practice Similar Questions
Test your understanding with related questions
1
Question 1In what scenario would using an RNN (like LSTM) be more advantageous than a traditional feedforward neural network?
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Practice
2
Question 2What is a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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Practice
3
Question 3What is a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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Practice
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