# 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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