Deep Learning and Reinforcement Learning Week 4 All Quiz

Practice: Convolutional Neural Networks 

1. Given the syntax below, select the option that will best improve a CNN model that you are trying to fit.
1. model.fit(x_train, y_train, batch_size=batch_size, epochs=100, validation_data=(x_test, y_test))

A) Remove the validation_data option.
B) Increase the number of epochs to 100.
C) ecrease the number of epochs to 50.
D) Add shuffling, by adding “, shuffle=True” at the end.

ANSWER= (D) Add shuffling, by adding “, shuffle=True” at the end.

 

2. Which of the following statements is TRUE about a kernel in a Convolutional Layer applied to an image?

A) Kernels allow the convolutional layers to perform nonlinear transformations.
B) Kernels detect local features in an image such as lines, corners, and edges.
C) Kernels identify which channel in the input data contains the most information.
D) Kernels ease computation by reducing the number of dimensions in an image that must be processed.

ANSWER= (B) Kernels detect local features in an image such as lines, corners, and edges.

Week 4 Final Quiz  

1. What is the main function of backpropagation when training a Neural Network?

A) Preprocess the input layer
B) Make adjustments to the weights
C) Make adjustments to the loss function
D) Propagate the output on the output layer

ANSWER= (B) Make adjustments to the weights

 

2. (True/False) The “vanishing gradient” problem can be solved using a different activation function

A) True
B) False

ANSWER= (A) True

 

3. (True/False) Every node in a neural network has an activation function.

A) True
B) False

ANSWER= (B) False

 

4. These are all activation functions except:

A) Sigmoid
B) Hyperbolic tangent
C) Leaky hyperbolic tangent
D) ReLu

ANSWER= (C) Leaky hyperbolic tangent

 

5. Deep Learning uses deep Neural Networks for all these uses, except

A) As an alternative to manual feature engineering
B) To uncover usually unobserved relationships in the data
C) Cases in which explainability is the main objective
D) As a classification and regression technique

ANSWER= (C) Cases in which explainability is the main objective

 

6. These are all activation functions except:

A) Tanh (hyperbolic tangent)
B) ReLU (Rectified Linear Unit
C) Softmax
D) Pruning

ANSWER= (D) Pruning

 

7. (True/False) Optimizer approaches for Deep Learning Regularization use gradient descent:

A) True
B) False

ANSWER= (B) False

 

8. Stochastic gradient descent is this type of batching method:

A) online learning
B) mini batch
C) full batch
D) stochastic batch

ANSWER= (A) online learning

 

9. (True/False) The main purpose of data shuffling during the training of a Neural Network is to aid convergence and use the data in a different order each epoch.

A) True
B) False

ANSWER= (A) True

 

10. Which of the following IS NOT a benefit of Transfer Learning?

A) Reducing time required to tune hyper-parameters
B) Reducing the impact of the vanishing gradient problem on early layers
C) Improving the speed at which large models can be trained from scratch
D) Conveying computational benefits when problems share similar primitive features.

ANSWER= (C) Improving the speed at which large models can be trained from scratch

 

14. Which of the following statements about using a Pooling Layer is TRUE?

A) Pooling can reduce both computational complexity and overfitting.
B) Pooling can reduce computational complexity, at the cost of overfitting.
C) Pooling increases computational complexity, but helps with overfitting.
D) Pooling reduces the likelihood of overfitting, but generally does not impact computational complexity.

ANSWER= (A) Pooling can reduce both computational complexity and overfitting.