Introduction to Computer Vision and Image Processing Week 4
Course Name: Introduction to Computer Vision and Image Processing
Course Link: Introduction to Computer Vision and Image Processing
These are answers of Introduction to Computer Vision and Image Processing Week 4 Quiz
Practice Assessment
Q1. Data augmentation techniques
Add extra layers to the neural network
Randomly change the training data
Prevent overfitting
Answer: Randomly change the training data, Prevent overfitting
Q2. What is the problem that occurs when a network gets deeper, resulting in a smaller gradient?
Validation
Gradient descent
Vanishing gradient
Answer: Vanishing gradient
Q3. Usually, the more input dimensions there are in a neural network, the…
More neurons are required
Less neurons are required
The number of required neurons remains the same
Answer: More neurons are required
These are answers of Introduction to Computer Vision and Image Processing Week 4 Quiz
Q4. Which of the following are activation functions? Check all that apply.
ReLU
Sigmoid
Logistic function
Answer: ReLU, Sigmoid, Logistic function
Q5. For a given input in a neural network, we obtain an output for each neuron for the last layer. The class chosen is according to the index of the neuron with the…
Largest value
Smallest value
Randomly selected value
Answer: Largest value
These are answers of Introduction to Computer Vision and Image Processing Week 4 Quiz
Graded Quiz
Q1. Layering multiple linear and logistic functions is best described as
Fully Connected Network
Logistic Regression
Non-Linear Network
Answer: Fully Connected Network
Q2. Which of the following helps to reduce the number of parameters of an input image and still preserves the important features?
Layer
Flattening
Receptive field
Pooling
Answer: Pooling
These are answers of Introduction to Computer Vision and Image Processing Week 4 Quiz
Q3. If I add more neurons to my neural network, what may I expect?
A perfect model
Overfitting
Underfitting
Answer: Overfitting
Q4. What makes a neural network a deep neural network?
Having one hidden layer
Having no hidden layer
An overfitting model
Having more than one hidden layer
Answer: Having more than one hidden layer
Q5. When we apply logistic regression in the context of Neural Networks, what is that called?
SoftMax Function
Activation Function
Linear Function
Decision Function
Answer: Activation Function
These are answers of Introduction to Computer Vision and Image Processing Week 4 Quiz
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