**Week 3 Comprehensive**

1.

Question 1

Which of the following indicates whether a doctor or machine is doing well at finding positive examples in a data set?

1 point

- Positive Predictive Value
- Likelihood Ratio
**Sensitivity**- Specificity

2.

Question 2

Which of the following is used to distinguish the false positive rate from the false negative rate?

1 point

- Sensitivity
- False Negative
- Negative Predictive Value
**Specificity**

3.

Question 3

Which of the following is the best conceptual definition of one dimensional convolution?

1 point

- “Inverting” of a shape, where the inversion matches a feature.
**“Sliding” of two signals, where a matched feature gives a high value of convolution.**- “Intertwining” of two signals, where one wraps around the other to form a feature.
- “Distortion” of one signal, according to the feature shape

4.

Question 4

Which of the following can a user choose when designing a convolutional layer? (Choose all that are correct.)

1 point

**Filter depth****Filter size****Filter number****Filter stride**- Filter weights

5.

Question 5

What is a fully connected readout?

1 point

- A layer with ten classifications.
- A layer with connections to all feature maps.
- The vectorization of a pooling layer.
**A layer with a single neuron for each output class.**

6.

Question 6

Why are nonlinear activation functions preferable?

1 point

- Nonlinear activation functions are preferable because they are used in generalized linear models in statistics.
**Nonlinear activation functions increase the functional capacity of the neural network by allowing the representation of nonlinear relationships between features in input.**- Nonlinear activation functions are preferable because they have been used historically.
- Nonlinear activation functions are NOT preferable to linear ones, as they lose information in systems with high variance.

7.

Question 7

Which of the following are benefits of pooling? (Choose all that are correct.)

1 point

**Decreases bias.****Combats overfitting.****Vectorizes the data.****Encourages translational invariance.**- Reduces computational complexity.

8.

Question 8

How are parameters that minimize the loss function found in practice?

1 point

- Fractal geometry
- Gradient descent
- Simplex algorithm
**Stochastic gradient descent**

9.

Question 9

Which of the following is an advantage of hierarchical representation of image features?

1 point

- Eliminating bias.
- Decreasing the computational complexity.
**Better leveraging all training data.**- Decreasing variance in the model.

10.

Question 10

Why does transfer learning work?

1 point

**Top-level features are specialized for a particular task, while low-level features are universal to all images.**- All layers of filters can be learned by studying the mammalian receptive fields.
- Low-level features are specialized for a particular task, while top-level features are universal to all images.
- All images are composed of pixels with three color channels.

**Important Links:**

**Introduction to Machine Coursera Week 1 Quiz****Introduction to Machine Coursera Week 2 Quiz****Introduction to Machine Coursera Week 3 Quiz****Introduction to Machine Coursera Week 4 Quiz**