# Deep Learning and Reinforcement Learning Week 8

**Course Name: Deep Learning and Reinforcement Learning**

**Course Link: Deep Learning and Reinforcement Learning**

#### These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz

### Practice: Variational Autoencoders

**Q1. (True/False) A common characteristic of both Autoencoders and Variational Autoencoders is that both have one neural network for encoding and another one for decoding.**

True

False

**Answer: True**

**Q2. These are all additional steps that you need to consider when using Variational Autoencoders, except:**

Incorporate a logarithmic term into the loss function

Use binary crossentropy

Remove a KL loss function

Consider parameters within a distribution

**Answer: Remove a KL loss function**

**These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz**

**Q3. Choose the right assertion in the context of comparing the reconstruction error of Autoencoders and Variational Autoencoders:**

The reconstruction error of **variational autoencoders** can be **higher** because variational autoencoders are designed to maximize the interpretability of the latent space, not to minimize the reconstruction error.

The reconstruction error of **variational autoencoders** can be **lower** because variational autoencoders are designed to maximize the interpretability of the latent space, not to minimize the reconstruction error.

The reconstruction error of **autoencoders** can be **lower** because autoencoders are designed to maximize the interpretability of the latent space, not to minimize the reconstruction error.

The reconstruction error of **autoencoders** can be **higher** because autoencoders are designed to maximize the interpretability of the latent space, not to minimize the reconstruction error.

**Answer: The reconstruction error of variational autoencoders can be higher because variational autoencoders are designed to maximize the interpretability of the latent space, not to minimize the reconstruction error.**

**These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz**

### Practice: Generative Adversarial Networks

**Q1. The development of Generative Adversarial Networks was motivated, in part, by**

the need for faster computation across multiple platforms.

the need to simultaneously generate differing types of output.

the vulnerability of standard Deep Learning approaches to input manipulation.

the inability of standard Deep Learning approaches to implement backpropagation.

**Answer: the vulnerability of standard Deep Learning approaches to input manipulation.**

**Q2. (True/False) GANs are a way of training two neural networks simultaneously.**

True

False

**Answer: True**

**Q3. (True/False) GANs are probably behind some applications like FaceApp and applications that can make you look older.**

True

False

**Answer: True**

**These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz**

### Final Quiz

**Q1. Select the right assertion:**

Autoencoders learn from a compressed representation of the data, while variational autoencoders learn from a probability distribution representing the data.

Variational autoencoders learn from a compressed representation of the data, while autoencoders learn from a probability distribution representing the data.

Autoencoders and Principal Component Analysis can be used interchangeably.

**Answer: Autoencoders learn from a compressed representation of the data, while variational autoencoders learn from a probability distribution representing the data.**

**Q2. (True/False) Variational autoencoders are generative models.**

True

False

**Answer: True**

**Q3. When comparing the results of Autoencoders and Principal Component Analysis, which approach might best improve the results from Autoencoders?**

Add labels to the data

Add layers and epochs

Add a Variational Autoencoder

Reduce the dimensions of the data

**Answer: Add layers and epochs**

**These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz**

**Q4. (True/False) KL loss is used in Variatoinal Autoencoders to represent the measure of the difference between two distributions.**

True

False

**Answer: True**

**Q5. A good way to compare the inputs and outputs of a Variational Autoencoder is to calculate the mean of a reconstruction function based on binary crossentropy.**

True

False

**Answer: True**

**Q6. The main parts of GANs architecture are:**

loss error and random noise

generated and adversarial neurons

adversarial and non adversarial neurons

generator and discriminator

**Answer: generator and discriminator**

**These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz**

**Q7. (True/False) One of the main advantages of GANs over other adversarial networks is that it does not spend any time evaluating whether an input or image is fake or real. It only computes probability of being fake.**

True

False

**Answer: True**

**These are answers of Deep Learning and Reinforcement Learning Week 8 Quiz**

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