WorksheetsDL Quiz1
Total questions: 15
Worksheet time: 8mins
What do the gradients of backpropagation compute?
Loss Function
Profit Function
Positive Function
Negative Function
Which rule is followed by the Backpropagation algorithm?
Static Rule
None
Dynamic Rule
Chain Rule
Error rates are reduced in backpropagation due to _____________?
Proper Tuning
Iteration
Generalization
Improper Tuning
How many layers does the backpropagation algorithm consist of?
Zero
Two
Three
One
Which is not the layer of the Backpropagation algorithm?
Input Layer
Output Layer
Hidden Layer
Intermediate Layer
Weights in backpropagation algorithms are updated _____________?
Forward
Backward
a & b
None
What happens to the Cost function when it meets the termination condition?
Maximize
Minimize
Increase
Enhance
Backpropagation can minimize ___________?
Profit Function
Positive Function
Cost Function
Negative Function
What adjusts the parameters of models in neural networks?
Backpropagation
Channel Propagation
Signal Propagation
Front Propagation
What is the role of the learning rate in the backpropagation process?
To set the number of epochs
To calculate the loss
To adjust the number of layers
To determine the speed of convergence
What is a Perceptron in the context of neural networks?
A type of activation function
A single-layer neural network
A deep learning framework
A machine learning algorithm
Which of the following is an example of an error function commonly used in training neural networks?
Mean Squared Error (MSE)
Gradient Descent
Sigmoid Function
ReLU Activation
What is the main objective of optimization algorithms in neural network training?
To minimize the training time
To maximize the number of hidden layers
To find the best initial weights and biases
To minimize the error or loss function
Backpropagation is a technique used for:
Forward pass in neural networks
Initializing weights in a neural network
Calculating gradients and updating weights in a neural network
Regularizing the weights in a neural network
Which of the following is a common problem associated with overfitting in neural networks?
High bias
Low variance
Generalization
Poor performance on unseen data
