Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Image Classification
B
Text Generation
C
Time Series Analysis
D
Speech Recognition
Understanding the Answer
Let's break down why this is correct
Answer
Recurrent Neural Networks are designed to handle data that changes over time, so they are naturally used for sequence prediction such as language modeling or speech recognition. Convolutional Neural Networks, on the other hand, scan data with sliding filters that detect local patterns and then combine them, making them ideal for recognizing spatial structure. This makes CNNs especially good for image classification, where the network learns to identify objects in pictures. For example, a CNN can take a photo of a cat and learn to output “cat” by recognizing edges, textures, and shapes that appear together. Thus the analogy is RNN: Sequence Prediction :: CNN: Image Classification.
Detailed Explanation
CNNs look at small patches of a picture and learn patterns that help decide what the whole picture shows. Other options are incorrect because The idea that CNNs generate new text is a mix‑up with RNNs, which remember words in order; Time series data can be fed to CNNs, but the network’s main strength is spotting patterns in space, not in time.
Key Concepts
Recurrent Neural Networks (RNN)
Convolutional Neural Networks (CNN)
Sequence Modeling
Topic
Recurrent Neural Networks (RNN)
Difficulty
medium 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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3
Question 3Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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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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Question 6Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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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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