Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
They can maintain a memory of previous inputs
B
They require less data for training
C
They are simpler to implement than feedforward networks
D
They eliminate the need for any preprocessing of data
Understanding the Answer
Let's break down why this is correct
Answer
Recurrent Neural Networks keep a hidden state that updates as each element of a sequence is processed, so they can remember information from earlier steps. This lets them model patterns that depend on past inputs, something a feedforward network can’t do because it treats all inputs independently. For example, when translating a sentence, an RNN can use the hidden state to remember the meaning of earlier words while processing later ones, whereas a feedforward net would need a fixed‑size input and would lose that context. Thus, RNNs can handle variable‑length sequences and capture temporal relationships more naturally than traditional feedforward networks.
Detailed Explanation
RNNs keep a hidden state that carries information from earlier inputs. Other options are incorrect because Some think RNNs need less data because they are clever; Many believe loops make RNNs easier to code.
Key Concepts
Recurrent Neural Networks
Sequence Modeling
Memory in Neural Networks
Topic
Recurrent Neural Networks (RNN)
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of financial forecasting, how can Recurrent Neural Networks (RNNs) be effectively utilized for sequence prediction in business intelligence applications?
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Question 2In what scenario would using an RNN (like LSTM) be more advantageous than a traditional feedforward neural network?
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3
Question 3What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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4
Question 4In the context of financial forecasting, how can Recurrent Neural Networks (RNNs) be effectively utilized for sequence prediction in business intelligence applications?
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5
Question 5In what scenario would using an RNN (like LSTM) be more advantageous than a traditional feedforward neural network?
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Question 6What is a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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7
Question 7What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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