
Gradient Decent
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is gradient descent?
A machine learning algorithm used for classification tasks.
An optimization algorithm used to minimize a function by iteratively adjusting the parameters.
A supervised learning technique used for regression problems.
A statistical approach for clustering data points into groups.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does gradient descent work?
It tries random parameter values and selects the one that yields the lowest loss.
It uses matrix operations to minimize the loss function.
It calculates the gradient of the loss function with respect to the parameters and updates the parameters in the opposite direction.
It calculates the gradient of the loss function with respect to the parameters and updates the parameters in the same direction.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of gradient descent?
To find the global minimum of a function.
To maximize the accuracy of a machine learning model.
To solve linear equations
To find the local minimum of a function.
4.
MULTIPLE SELECT QUESTION
45 sec • 1 pt
What is the role of the learning rate in gradient descent?
It determines the speed at which the model learns and converges to the optimal solution.
It defines the size of each step taken during the optimization process.
It influences how quickly the model adapts to changes in the input data.
All of the above.
5.
MULTIPLE SELECT QUESTION
45 sec • 1 pt
What is the difference between batch gradient descent and stochastic gradient descent?
(select two best)
In batch gradient descent, all data points are considered for each parameter update, while in stochastic gradient descent, only one data point is used.
Batch gradient descent is faster but less accurate compared to stochastic gradient descent.
Stochastic gradient descent is suitable for large datasets, while batch gradient descent is preferred for small datasets.
Batch gradient descent more accurate compared to stochastic gradient descent.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is momentum-based gradient descent?
A variant of gradient descent that introduces a momentum term to accelerate convergence and dampen oscillations.
A technique that adjusts the learning rate dynamically based on the magnitude of the gradients.
An optimization algorithm that computes the gradients of the loss function using only a subset of the training data.
A method for regularizing neural networks to prevent overfitting.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
There are how many types of Gradient Descent?
1
2
3
4
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