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 context, like the meaning of a word in a sentence or the rhythm of a song. Unlike a feedforward network, which treats all inputs as independent, an RNN can handle sequences of any length without needing a fixed-size input. For example, when predicting the next word in a sentence, an RNN uses the hidden state to remember earlier words, improving accuracy over a feedforward model that would only look at the current word. This ability to capture temporal dependencies is the main advantage of RNNs for sequence data.
Detailed Explanation
Recurrent Neural Networks keep a hidden state that updates with each new input. Other options are incorrect because Some think RNNs need less data, but they actually need many examples to learn the patterns in sequences; RNNs are not simpler; they have loops that feed outputs back into the network.
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 a key advantage of using Recurrent Neural Networks over traditional feedforward networks for sequence data?
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4
Question 4What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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5
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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6
Question 6In what scenario would using an RNN (like LSTM) be more advantageous than a traditional feedforward neural network?
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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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