Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
They maintain hidden states that capture previous information.
B
They can process all input data simultaneously.
C
They only work with numerical data.
D
They are less complex than feedforward neural networks.
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 arrives, so they can remember past information while processing new data. This hidden state acts like a short‑term memory that captures dependencies between earlier and later parts of the input. Because the network processes inputs one step at a time, it naturally handles variable‑length sequences and learns patterns that depend on position or context. For example, when translating a sentence, an RNN can remember the subject that appeared earlier to decide the correct verb form later. Thus, RNNs are especially good at tasks where the order and context of data matter.
Detailed Explanation
RNNs keep a hidden state that carries information from earlier steps. Other options are incorrect because Some think RNNs can read all input at once, but they actually read one element after another; RNNs do not only handle numeric data.
Key Concepts
Recurrent Neural Networks
Sequence Modeling
Hidden States
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 2Recurrent Neural Networks (RNN) : Sequence Prediction :: Convolutional Neural Networks (CNN) : ?
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Question 3Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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Question 4What is a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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Question 5In 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 6Recurrent Neural Networks (RNN) : Sequence Prediction :: Convolutional Neural Networks (CNN) : ?
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Question 7Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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Question 8What is a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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Question 9What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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