Deep Learning and Reinforcement Learning Week 4
Course Name: Deep Learning and Reinforcement Learning
Course Link: Deep Learning and Reinforcement Learning
These are answers of Deep Learning and Reinforcement Learning Week 4 Quiz
Practice: Convolutional Neural Networks
Q1. Given the syntax below, select the option that will best improve a CNN model that you are trying to fit.
model.fit(x_train, y_train, batch_size=batch_size, epochs=100, validation_data=(x_test, y_test))
Remove the validation_data option.
Increase the number of epochs to 100.
Decrease the number of epochs to 50.
Add shuffling, by adding “, shuffle=True” at the end.
Answer: Add shuffling, by adding “, shuffle=True” at the end.
Q2. Which of the following statements is TRUE about a kernel in a Convolutional Layer applied to an image?
Kernels allow the convolutional layers to perform nonlinear transformations.
Kernels detect local features in an image such as lines, corners, and edges.
Kernels identify which channel in the input data contains the most information.
Kernels ease computation by reducing the number of dimensions in an image that must be processed.
Answer: Kernels detect local features in an image such as lines, corners, and edges.
These are answers of Deep Learning and Reinforcement Learning Week 4 Quiz
Final Quiz
Q1. What is the main function of backpropagation when training a Neural Network?
Preprocess the input layer
Make adjustments to the weights
Make adjustments to the loss function
Propagate the output on the output layer
Answer: Make adjustments to the weights
Q2. (True/False) The “vanishing gradient” problem can be solved using a different activation function.
True
False
Answer: True
Q3. (True/False) Every node in a neural network has an activation function.
True
False
Answer: True
These are answers of Deep Learning and Reinforcement Learning Week 4 Quiz
Q4. These are all activation functions except:
Sigmoid
Hyperbolic tangent
Leaky hyperbolic tangent
ReLu
Answer: Leaky hyperbolic tangent
Q5. Deep Learning uses deep Neural Networks for all these uses, excep
As an alternative to manual feature engineering
To uncover usually unobserved relationships in the data
Cases in which explainability is the main objective
As a classification and regression technique
Answer: Cases in which explainability is the main objective
Q6. These are all activation functions for CNN, except:
Regularization penalty in cost function
Dropout
Early stopping
Pruning
Answer: Pruning
These are answers of Deep Learning and Reinforcement Learning Week 4 Quiz
Q7. (True/False) Optimizer approaches for Deep Learning Regularization use gradient descent:
True
False
Answer: False
Q8. Stochastic gradient descent is this type of batching method:
online learning
mini batch
full batch
stochastic batch
Answer: online learning
Q9. (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.
True
False
Answer: True
These are answers of Deep Learning and Reinforcement Learning Week 4 Quiz
Q10. Which of the following IS NOT a benefit of Transfer Learning?
Reducing time required to tune hyper-parameters
Reducing the impact of the vanishing gradient problem on early layers
Improving the speed at which large models can be trained from scratch
Conveying computational benefits when problems share similar primitive features.
Answer: Improving the speed at which large models can be trained from scratch
Q11. Which of the following statements about using a Pooling Layer is TRUE?
Pooling can reduce both computational complexity and overfitting.
Pooling can reduce computational complexity, at the cost of overfitting.
Pooling increases computational complexity, but helps with overfitting.
Pooling reduces the likelihood of overfitting, but generally does not impact computational.
Answer: Pooling can reduce both computational complexity and overfitting.
These are answers of Deep Learning and Reinforcement Learning Week 4 Quiz
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