## In this blog you will find the correct answer of the Coursera quiz Introduction to Machine Coursera Week 3 Quiz mixsaver always try to brings best blogs and best coupon codes

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
• Simplex algorithm

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.