Which of the following are types of CNN architecture? Check all that apply:
1. Question 1 Which of the following are types of CNN architecture? Check all that apply: 1 / 1 point VGGNet AlexNet RavNe JoNet
If I add more neurons to my neural network, what may I expect?
3. Question 3 If I add more neurons to my neural network, what may I expect? 1 / 1 point Overfitting Underfitting A perfect model
Look at the figure below, what kind of activation function is this:
2. Question 2 Look at the figure below, what kind of activation function is this: 1 / 1 point Sigmoid ReLU Logistic function
A lot of times, we don’t have the time and resources to build our own model, so we use pre-trained CNN’s to speed up the process. This is best described as…
5. Question 5 A lot of times, we don’t have the time and resources to build our own model, so we use pre-trained CNN’s to speed up the process. This…
What makes a neural network a deep neural network?
4. Question 4 What makes a neural network a deep neural network? 1 / 1 point Having one hidden layer Having no hidden layer An overfitting model Having more…
Layering multiple linear and logistic functions is best described as
1. Question 1 Layering multiple linear and logistic functions is best described as 1 / 1 point Non-Linear Network Logistic Regression Fully Connected Network
How many colour channels does a Grayscale image have ?
1. Question 1 How many colour channels does a Grayscale image have ? 1 point 1 2 3 4
You select the learning rate
4. Question 4 You select the learning rate 1 point Randomly Using the validation date The learning rate should always be one
When classifying images into tiger and lions, which image classification algorithm finds the best plane that divides a dataset into two classes?
3. Question 3 When classifying images into tiger and lions, which image classification algorithm finds the best plane that divides a dataset into two classes? 1 / 1 point K-Nearest Neighbours …
You train a Support Vector Machine and obtain an accuracy of 100% on the training data and 50% on the validation data. This is an example of:
1. Question 1 You train a Support Vector Machine and obtain an accuracy of 100% on the training data and 50% on the validation data. This is an example of:…
