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 are built to remember information from earlier parts of a sequence by passing a hidden state from one step to the next. This hidden state acts like a short‑term memory that updates as each new element arrives, letting the network keep track of what has happened before. Because of this, RNNs can use past inputs to influence the prediction of future ones, which is essential for tasks like language modeling or speech recognition. For example, when predicting the next word in a sentence, an RNN can use the hidden state that summarizes all previous words to choose a word that fits the context. Thus, their ability to carry information across time makes them ideal for sequence modeling.
Detailed Explanation
RNNs keep a hidden state that remembers what happened before. Other options are incorrect because The idea that RNNs can look at all data at once is a misunderstanding; Thinking that RNNs only work with numbers is incorrect.
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 5What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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Question 6In 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 7Recurrent Neural Networks (RNN) : Sequence Prediction :: Convolutional Neural Networks (CNN) : ?
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Question 8Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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Question 9What is a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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