Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Input data → Generate hidden state → Output prediction
B
Generate hidden state → Input data → Output prediction
C
Output prediction → Input data → Generate hidden state
D
Input data → Output prediction → Generate hidden state
Understanding the Answer
Let's break down why this is correct
Answer
When an RNN processes a sequence, it first receives the first input element and combines it with an initial hidden state, usually set to zero. It then updates the hidden state by applying a nonlinear transformation that mixes the current input with the previous hidden state, producing a new hidden state that carries information from all past inputs. This updated hidden state is used to generate an output for that time step, often through a separate linear layer. The process repeats for each subsequent input, creating a chain of hidden states that encode the entire sequence. During training, the network learns by back‑propagating errors through this chain (back‑propagation through time), adjusting weights so that the hidden states capture useful patterns, such as predicting the next word in a sentence.
Detailed Explanation
An RNN reads the sequence one piece at a time. Other options are incorrect because It sounds like the RNN could know something before seeing any data, but it cannot; The prediction cannot come before any data.
Key Concepts
Recurrent Neural Networks
Sequence Modeling
Hidden States
Topic
Recurrent Neural Networks (RNN)
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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Question 2What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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Question 3What is the correct sequence of steps in the process of using a sequence transduction model for translating input sequences into output sequences?
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Question 4Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.
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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 6What is the correct sequence of operations when applying an RNN to model a sequence of data?
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Question 7What is the correct sequence of steps in the process of using a sequence transduction model for translating input sequences into output sequences?
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