# Deep Learning and Reinforcement Learning Week 1

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

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

#### These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz

### Practice: Introduction to Neural Networks

**Q1. Neural networks and Deep Learning are behind many of the AI applications that are part of our daily lives.**

True

False

**Answer: True**

**Q2. Which one of the following is true in terms of the difference between grid search and randomized search?**

Grid search is more efficient than randomized search.

The points in the randomized search space are more evenly distributed than the points in the grid search space.

Randomized search selects random combinations of parameters to train a model, whereas grid search goes through all combinations.

Randomized search goes through a more exhaustive search for selecting a model than grid search.

**Answer: Randomized search selects random combinations of parameters to train a model, whereas grid search goes through all combinations.**

**Q3. This is a characteristic that neural networks and logistic regression have in common:**

both models retain easy explainability for their computational outcomes.

both models use only linear functions.

the weights, inputs, and bias of neural networks are the equivalent to the coefficients, variables, and constant of a logistic regression

both models make use of layers of units of computation.

**Answer: the weights, inputs, and bias of neural networks are the equivalent to the coefficients, variables, and constant of a logistic regression**

**These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz**

### Practice: Optimization and Gradient Descent

**Q1. Select all the methods that can be used to minimize a cost function:**

mini-batch gradient descent

stochastic gradient descent

batch gradient descent

**Answer: mini-batch gradient descent, stochastic gradient descent, batch gradient descent**

**Q2. How many sample(s) are used in a stochastic gradient descent?**

Answer: 1

**Q3. Which method uses all the samples in one iteration to update model parameters?**

Batch gradient descent

Stochastic gradient descent

Mini-batch gradient descent

**Answer: Batch gradient descent**

**These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz**

### Final Quiz

**Q1. What is another name for the “neuron” on which all neural networks are based?**

deep neuron

sigmoid

neutron

perceptron

**Answer: perceptron**

**Q2. What is an advantage of using a network of neurons?**

The network is not limited to using only the sigmoid function as an activation function.

A network of neurons can represent a non-linear decision boundary.

Feedforward capabilities are limited.

The output of neurons can be averaged.

**Answer: A network of neurons can represent a non-linear decision boundary.**

**Q3. A dataset with 8 features would have how many nodes in the input layer?**

10

2

4

8

**Answer: 8**

**These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz**

**Q4. For a single data point, the weights between an input layer with 3 nodes and a hidden layer with 4 nodes can be represented by a:**

4 x 3 matrix

3 x 4 matrix.

4 x 4 matrix

3 x 3 matrix

**Answer: 3 x 4 matrix.**

**Q5. Use the following image for reference. How many hidden layers are in this Neural Network?**

Two

Four

Eight

Fourteen

**Answer: Two**

**These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz**

**Q6. Use the following image for reference. How many hidden units are in this Neural Network?**

Two

Four

Eight

Fourteen

**Answer: Eight**

**These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz**

**Q7. Which statement is TRUE about the relationship between Neural Networks and Logistic Regression?**

A Neural Network is less likely to overfit to training data than Logistic Regression.

A Neural Network with two or more deep layers will likely outperform Logistic Regression.

A Multi-Layer Perceptron is equivalent to Logistic Regression if all activation functions are the same.

A single-layer Neural Network can be parameterized to generate results equivalent to Linear or Logistic Regression.

**Answer: A single-layer Neural Network can be parameterized to generate results equivalent to Linear or Logistic Regression.**

**These are answers of Deep Learning and Reinforcement Learning Week 1 Quiz**

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