What is the correct sequence of operations when applying an RNN to model a sequence of data?

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Input data → Generate hidden state → Output prediction

Generate hidden state → Input data → Output prediction

Output prediction → Input data → Generate hidden state

Input data → Output prediction → Generate hidden state

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Answer

When you feed a sequence into an RNN, you first start with an initial hidden state, often zeros. At each time step you take the current input element and the previous hidden state, combine them (usually by a weighted sum plus bias), and pass this through an activation function to produce a new hidden state. That new hidden state can be used immediately to generate an output for that step, or stored for later use. You then move to the next input in the sequence and repeat the process, updating the hidden state each time. For example, if the sequence is “A, B, C,” you start with h₀=0, compute h₁ from A, h₂ from B and h₁, and h₃ from C and h₂, producing outputs at each step.

Detailed Explanation

First the RNN reads the input data. Other options are incorrect because It says the hidden state is made before any data arrives; It puts the prediction before the data.

Key Concepts

Recurrent Neural Networks

Sequence Modeling

Hidden States

Topic

Recurrent Neural Networks (RNN)

Difficulty

hard level question

Cognitive Level

understand

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Which of the following statements are true regarding Recurrent Neural Networks (RNNs)? Select all that apply.

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What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?

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