18 Questions
What is the main idea behind Occam's Razor?
Among competing hypotheses, the simplest is the best
What is the purpose of regularization in machine learning models?
To prevent overfitting and make the model simpler
What is the common value of the probability of dropping in dropout regularization?
0.5
What is the type of regularization that corresponds to MAP inference using a Gaussian prior on W?
L2 regularization
What is the purpose of the hyperparameter in L2 regularization?
To control the regularization strength
What is the type of regularization that combines both L1 and L2 regularization?
Elastic net regularization
What is the primary reason for using an activation function in a neural network?
To introduce non-linearity in the neural network
What is the main advantage of using a deep representation learning approach?
It reduces the need for feature engineering
What is the primary purpose of the W2 matrix in the 2-layer neural network equation F = W2max(0,W1x)?
To output the final scores for the classes
What is the main challenge in designing a neural network architecture?
Selecting the optimal hyperparameters
What is the primary difference between a 2-layer and a 3-layer neural network?
The number of hidden layers
What is the effect of multiplying the weights by a constant factor in a linear classifier?
The loss function will remain unchanged
What is the primary purpose of regularization techniques in neural networks?
To reduce the risk of overfitting
What is the main objective of regularization techniques in machine learning?
To prevent the model from overfitting
What is the underlying principle of Occam's Razor in machine learning?
The model with the simplest architecture is preferred
What is the primary principle behind Occam's Razor in the context of neural networks?
The model with the least complexity is always the best
What is the effect of L1 regularization on the model's weights?
The weights are pushed towards zero
What is the Elastic Net regularization technique?
A combination of L1 and L2 regularization
Test your understanding of data loss and regularization in machine learning models. Learn how to improve model predictions and avoid overfitting with these essential concepts.
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