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WorksheetsDeep learning Batch 1
Total questions: 10
Worksheet time: 2mins
In the context of tensors, what is the order of a matrix?
A) 0th-order
B) 1st-order
C) 2nd-order
D) 3rd-order
Which of the following is true about the probability density function (PDF) for continuous variables?
A) The PDF must satisfy \( p(x) \leq 1 \)
B) The integral of the PDF over its domain must equal 1
C) The PDF can take negative values
D) The PDF is only defined for discrete variables
What is the primary difference between Batch Gradient Descent and Stochastic Gradient Descent (SGD)?
A) SGD uses the entire dataset for each update, while Batch GD uses a single example
B) SGD uses a single example for each update, while Batch GD uses the entire dataset
C) SGD is slower but more accurate than Batch GD
D) SGD is only used for convex functions
Which of the following is a key disadvantage of Stochastic Gradient Descent (SGD)?
A) It requires large memory to compute gradients
B) It has high variance in parameter updates
C) It converges slower than Batch Gradient Descent
D) It cannot escape local minima
What is the main purpose of the learning rate in gradient-based optimization?
A) To control the speed of convergence
B) To increase the number of iterations
C) To reduce the loss function directly
D) To increase the model complexity
Which of the following is true about L1 regularization?
A) It penalizes the square of the weights
B) It is less effective than L2 regularization
C) It is also known as weight decay
D) It can reduce some weights to exactly zero
What is the primary purpose of dropout in neural networks?
A) To reduce the number of layers in the network
B) To increase the learning rate
C) To randomly remove nodes during training to prevent overfitting
D) To reduce the number of parameters in the model
Which of the following is a common technique to reduce overfitting in deep learning?
A) Increasing the number of layers
B) Decreasing the amount of training data
C) Using dropout
D) Increasing the learning rate
What is the main challenge of using a very high learning rate in gradient descent?
A) It may cause the algorithm to overshoot the minimum
B) It will always converge to the global minimum
C) It will reduce the number of iterations required
D) It will increase the model's bias
Which of the following is true about the Adam optimizer?
A) It uses a fixed learning rate for all parameters
B) It combines the benefits of Adagrad and RMSprop
C) It is less efficient than SGD
D) It does not use momentum
