Q. In the context of neural networks, what is 'overfitting'?
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A.
When the model performs well on training data but poorly on unseen data
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B.
When the model has too few parameters
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C.
When the model is too simple to capture the data patterns
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D.
When the model converges too quickly
Solution
Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization on new data.
Correct Answer:
A
— When the model performs well on training data but poorly on unseen data
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Q. What is the primary advantage of using convolutional neural networks (CNNs) for image processing?
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A.
They require less data
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B.
They can capture spatial hierarchies
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C.
They are easier to train
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D.
They use fewer parameters
Solution
CNNs are designed to capture spatial hierarchies in images, making them highly effective for image processing tasks.
Correct Answer:
B
— They can capture spatial hierarchies
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Q. What is the primary function of the activation function in a neural network?
-
A.
To initialize weights
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B.
To introduce non-linearity
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C.
To optimize the learning rate
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D.
To reduce overfitting
Solution
The activation function introduces non-linearity into the model, allowing it to learn complex patterns.
Correct Answer:
B
— To introduce non-linearity
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Q. What is the purpose of the loss function in a neural network?
-
A.
To measure the accuracy of the model
-
B.
To quantify the difference between predicted and actual outputs
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C.
To optimize the learning rate
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D.
To determine the number of layers
Solution
The loss function quantifies how well the neural network's predictions match the actual target values.
Correct Answer:
B
— To quantify the difference between predicted and actual outputs
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Q. What is the role of the optimizer in training a neural network?
-
A.
To select the activation function
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B.
To adjust the weights based on the loss function
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C.
To determine the architecture of the network
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D.
To preprocess the input data
Solution
The optimizer adjusts the weights of the network based on the gradients calculated from the loss function.
Correct Answer:
B
— To adjust the weights based on the loss function
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Q. Which of the following is a common method for evaluating the performance of a neural network?
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A.
Confusion matrix
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B.
Gradient descent
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C.
Batch normalization
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D.
Dropout
Solution
A confusion matrix is used to evaluate the performance of a classification model by showing true vs. predicted classifications.
Correct Answer:
A
— Confusion matrix
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Q. Which of the following optimizers is known for adapting the learning rate during training?
-
A.
SGD
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B.
Adam
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C.
RMSprop
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D.
Adagrad
Solution
Adam (Adaptive Moment Estimation) adapts the learning rate based on the first and second moments of the gradients.
Correct Answer:
B
— Adam
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Q. Which of the following techniques is commonly used to prevent overfitting in neural networks?
-
A.
Increasing the learning rate
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B.
Using dropout
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C.
Reducing the number of layers
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D.
Using a linear activation function
Solution
Dropout is a regularization technique that randomly sets a fraction of the neurons to zero during training to prevent overfitting.
Correct Answer:
B
— Using dropout
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