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L1 regularization

L2 regularization

Both L1 and L2 regularization

Stop-word removal

Stemming and Lemmatization

One-hot encoding

All of the above

Mean Squared Error (MSE)

Binary Cross Entropy

Categorical Cross Entropy

Mean Absolute Error (MAE)

Increasing the number of neurons in each layer

Reducing the number of layers in the network

Regularization

Using a larger batch size during training

The model may become too complex and overfit the data

The model may become too simple and underfit the data

The model may converge more quickly during training

The model may become more computationally efficient

It is a supervised learning algorithm

It updates the weights in the neural network using the gradient of the loss function with respect to the weights

It is only used for binary classification problems

It is an unsupervised learning algorithm

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Machine-learning Questions Set 9:

Which of the following is a commonly used activation function for the hidden layers in neural networks?

Solution:

Which of the following is a commonly used activation function for the output layer in regression problems?

Solution:

Which of the following is a commonly used performance metric to evaluate the performance of SVM?

Solution:

Which of the following is an activation function used in neural networks that maps inputs to outputs between 0 and 1?

Solution:

Which of the following is a commonly used regularization technique in neural networks?

Solution:

Which of the following is a commonly used technique for pre-processing text data before training a neural network?

Solution:

Which of the following is a commonly used loss function in binary classification problems in neural networks?

Solution:

Which of the following is a common technique used to prevent overfitting in neural networks?

Solution:

Which of the following is a disadvantage of using a large number of hidden layers in a neural network?

Solution:

Which of the following is true about backpropagation in neural networks?

Solution:

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