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
  • 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.

 

 

 

 

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