In this blog you will find the correct answer of the Coursera quiz Introduction to Machine Coursera Week 2 Quiz mixsaver always try to brings best blogs and best coupon codes
 

Week 2 Comprehensive

 

1.
Question 1
What does the equation for the loss function do conceptually?

1 point

  • Mathematically define network outputs
  • Penalize overconfidence
  • Ignore historical statistical developments
  • Reward indecision

2.
Question 2
What is overfitting?

1 point

  • Overfitting refers to the fact that more complexity is always better, which is why deep learning works.
  • Model complexity fits too well to training data and will not generalize in the real-world.
  • Model complexity is perfectly matched to the data.
  • Model complexity is not enough to capture the nuance of the data and will under-perform in the real-world.

3.
Question 3
Why should the test set only be used once?

1 point

  • More than one use can lead to bias.
  • More than one use can lead to overfitting.
  • The model cannot learn anything new from subsequent uses.
  • It is expensive to use more than once.

4.
Question 4
Which two of the following describe the purpose of a validation set?

1 point

  • To estimate the performance of a model.
  • To pick the best performing model.
  • To test the performance in lieu of real-world data.
  • To learn the model parameters.

5.
Question 5
How do we learn our network?

1 point

  • Gradient descent
  • Downhill skiing
  • Monte Carlo simulation
  • Analytically determine global minimum

6.
Question 6
What technique is used to minimize loss for a large data set?

1 point

  • Newton’s method
  • Taylor series expansion
  • Stochastic gradient descent
  • Gradient descent

7.
Question 7
Which of the following are benefits of stochastic gradient descent?

1 point

  • With stochastic gradient descent, the update time does not scale with data size.
  • Stochastic gradient descent finds the solution more accurately.
  • Stochastic gradient descent can update many more times than gradient descent.
  • Stochastic gradient descent gets near the solution quickly.
  • Stochastic gradient descent finds a more exact gradient than gradient descent.

8.
Question 8
Why is gradient descent computationally expensive for large data sets?

1 point

  • Large data sets do not permit computing the loss function, so a more expensive measure is used.
  • Calculating the gradient requires looking at every single data point.
  • Large data sets require deeper models, which have more parameters.
  • There are too many local minima for an algorithm to find.

9.
Question 9
What are the two main benefits of early stopping?

1 point

  • It helps save computation cost.
  • It performs better in the real world.
  • It improves the training loss.
  • There is rigorous statistical theory on it.

10.
Question 10
Why are optimization and validation at odds?

1 point

  • Optimization seeks to do as well as possible on a training set, while validation seeks to generalize to the real world.
  • Optimization seeks to generalize to the real world, while validation seeks to do as well as possible on a validation set.
  • Optimization seeks to do as well as possible on a training set, while validation seeks to do as well as possible on a validation set.
  • They are not at odds—they have the same goal.

 

 

 

 

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