What is the primary goal when adjusting parameters in a binary classification problem?
Deep Learning CNN Convolutional Neural Networks with Python - Gradient Descent

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
To increase the number of parameters
To ensure the output matches the desired result
To reduce the number of data points
To make the algorithm more complex
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How are the values alpha, beta, and gamma determined when updating parameters?
By using the gradient vector
By randomly selecting values
By increasing the loss
By decreasing the number of parameters
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens when you take a step in the negative gradient direction?
The loss decreases
The parameters become zero
The loss remains the same
The loss increases
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the significance of the step size in gradient descent?
It affects the speed of convergence
It decreases the number of data points
It determines the number of parameters
It increases the loss
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is guaranteed if the loss function is convex in gradient descent?
The algorithm will not converge
The global optimum is achieved
The parameters will not change
A local minimum is found
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which algorithm is most commonly used in training neural networks?
Random Forest
Gradient Descent
Support Vector Machine
K-Nearest Neighbors
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main purpose of using gradient descent in machine learning?
To find the best parameters by reducing loss
To increase the complexity of the model
To ensure the model is overfitting
To increase the number of data points
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