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Introduction to Recurrent Neural Networks
Introduction to Recurrent Neural Networks Quiz
Question 1
What is the main characteristic of a Recurrent Neural Network (RNN)?
It processes data sequentially and has loops in its architecture
It processes data in parallel and has no loops
It uses a single-layer architecture for prediction
It is designed for image classification tasks
Question 2
Which of the following is a key use case of Recurrent Neural Networks (RNNs)?
Image generation
Sequential data prediction, such as time series forecasting
Dimensionality reduction
Object detection in images
Question 3
How does the RNN handle sequential data?
It uses a sliding window to extract features
It processes the data point by point, maintaining a memory of past inputs
It averages the inputs across the sequence
It applies a convolution to all the data points
Question 4
Which of the following is a major limitation of traditional RNNs?
They struggle to handle non-sequential data
They suffer from the vanishing gradient problem
They are too fast to train
They can only process data in batches
Question 5
Which type of RNN is designed to overcome the vanishing gradient problem?
Simple RNN
LSTM (Long Short-Term Memory)
GRU (Gated Recurrent Unit)
Both b and c
Question 6
What does the "hidden state" in an RNN represent?
The final output of the RNN
The input to the next layer in the network
The information retained from previous time steps
The weight updates during training
Question 7
In the context of RNNs, what does "unrolling" refer to?
Training the model with a large batch size
Unfolding the RNN across time steps to process the entire sequence
Reducing the number of layers in the network
Implementing dropout for regularization
There are 7 questions to complete.
