Welcome to your ultimate guide for Structuring Machine Learning Projects Coursera quiz answers! Whether you’re working through practice quizzes to solidify your understanding or preparing for graded quizzes to assess your knowledge, this guide has you covered.
Covering all course modules, this resource will help you apply best practices for organizing and managing machine learning projects, from problem scoping to model deployment.
Structuring Machine Learning Projects Coursera Quiz Answers – Practice & Graded Quizzes for All Modules
Table of Contents
Structuring Machine Learning Projects Module 01 Quiz Answers
Q1: Having three evaluation metrics makes it harder for you to quickly choose between two different algorithms, and will slow down the speed with which your team can iterate. True/False?
Answer: True
Explanation: Having multiple evaluation metrics can make decision-making more complex because the team has to optimize for multiple objectives at once, slowing down iterations.
Q2: If you had the three following models, which one would you choose?
Answer: A
Explanation: Model A is the best choice as it meets all the criteria: high test accuracy (97%), very fast runtime (1 second), and very small memory size (3MB). These attributes meet the City’s requirements of high accuracy, fast runtime, and small memory usage.
Q3: Based on the city’s requests, which of the following would you say is true?
Answer: Accuracy, running time, and memory size are all satisficing metrics because you have to do sufficiently well on all three for your system to be acceptable.
Explanation: The City requires a system that meets a threshold for accuracy, runtime, and memory size, so all three metrics are considered satisficing (not optimizing).
Q4: Which of these is the best choice for structuring your data into train/dev/test sets?
Answer: B
Explanation: A good train/dev/test split ensures that there is enough data in each set, with a larger proportion allocated for training and a reasonable amount for validation and testing. Option B provides a balanced split.
Q5: Should you add the citizens’ data to the training set
Answer: False
Explanation: Adding data from the citizens would change the distribution of the training set, which could lead to a mismatch between the training set and the dev/test sets, affecting performance on real-world data.
Q6: Why shouldn’t you add the citizens’ data to the test set?
Answer: The test set no longer reflects the distribution of data (security cameras) you most care about.
Explanation: The test set should represent the same distribution as the data the model will encounter in production (images from security cameras), and adding citizens’ data would skew the distribution.
Q7: Do you agree with the suggestion to train a bigger network to drive down the 4.0% training error?
Answer: No, because this shows your variance is higher than your bias.
Explanation: The training error is already low, indicating the model fits the training data well. The higher dev set error suggests the model is overfitting to the training data (high variance), not underfitting (high bias).
Q8: How would you define “human-level performance” in this scenario?
Answer: 0.4% (average of 0.3 and 0.5)
Explanation: To define “human-level performance,” it’s reasonable to take the average of the expert accuracies (0.3% and 0.5%) to estimate the error humans are likely to make.
Q9: Which of the following statements do you agree with?
Answer: A learning algorithm’s performance can be better than human-level performance but it can never be better than Bayes error.
Explanation: Bayes error is the lowest possible error for a given problem. While an algorithm can perform better than humans, it cannot surpass the theoretical limit of Bayes error.
Q10: Based on the evidence you have, which two of the following four options seem the most promising to try?
Answer:
- Try decreasing regularization.
- Get a bigger training set to reduce variance.
Explanation:
The training error is already low, so it suggests the model may be under-regularized. Also, increasing the training set size can help reduce variance and improve generalization.
Q11: What does it mean if the test set error is 7% when human-level performance is 0.1%, and training and dev errors are low?
Answer:
- You have overfit to the dev set.
- You should try to get a bigger dev set.
Explanation:
The large difference between dev and test errors suggests overfitting to the dev set. Also, increasing the dev set can help provide a more accurate estimate of performance.
Q12: What can you conclude after achieving training and dev set error of 0.05%, and human-level performance is 0.1%?
Answer:
- It is now harder to measure avoidable bias, thus progress will be slower going forward.
- If the test set is big enough for the 0.05% error estimate to be accurate, this implies Bayes error is ≤ 0.05.
Explanation:
Once you approach human-level performance, it becomes increasingly difficult to make significant progress, and it suggests that the remaining error is due to Bayes error.
Q13: What should you do if your system is performing well, but has more false negatives than your competitor’s system?
Answer: Rethink the appropriate metric for this task, and ask your team to tune to the new metric.
Explanation: If false negatives are more important to the City, you should reconsider your evaluation metric and optimize for that.
Q14: What should you do first when you have 1,000 images of a new bird species?
Answer: Try data augmentation/data synthesis to get more images of the new type of bird.
Explanation: Data augmentation is a good first step to handle the small dataset. You can generate more training data by applying transformations to the 1,000 images.
Q15: Which of the statements about your new Cat detector are true?
Answer:
- Buying faster computers could speed up your teams’ iteration speed and thus your team’s productivity.
- Needing two weeks to train will limit the speed at which you can iterate.
Explanation:
Faster computers and reducing the time to train the model are important to speed up iterations and testing of new ideas.
Structuring Machine Learning Projects Module 02 Quiz Answers
Q1: What is the first thing you do in the self-driving car project?
Answer: Spend a few days training a basic model and see what mistakes it makes.
Explanation: Getting a basic model up and running quickly helps identify the main issues in your approach early on, allowing for faster iteration.
Q2: For the output layer, a softmax activation would be a good choice for the output layer because this is a multi-task learning problem. True/False?
Answer: False
Explanation: A softmax activation is suitable for multi-class classification problems, but not for multi-task learning. In this case, since you’re detecting different objects (road signs, traffic signals), a separate sigmoid activation for each class is more appropriate.
Q3: Which dataset should you manually go through and carefully examine?
Answer: 500 images on which the algorithm made a mistake.
Explanation: The mistakes made by the algorithm are the most informative for error analysis. Examining these images can provide insights into where the model is going wrong and help refine the model.
Q4: If one example is equal to [0 ? 1 1 ?] then the learning algorithm will not be able to use that example. True/False?
Answer: True
Explanation: For multi-task learning, all tasks need to be fully labeled. If there are any “don’t care” labels (represented by ‘?’), the algorithm cannot process the example effectively.
Q5: How should you split the dataset into train/dev/test sets?
Answer: Mix all the 100,000 images with the 900,000 images you found online. Shuffle everything. Split the 1,000,000 images dataset into 600,000 for the training set, 200,000 for the dev set, and 200,000 for the test set.
Explanation: Shuffling the images from both sources and splitting them into appropriate training, dev, and test sets ensures the model is exposed to a diverse set of data while maintaining an unbiased evaluation.
Q6: Based on the data split, which statements are True?
Answer:
- You have a large avoidable-bias problem because your training error is quite a bit higher than the human-level error.
- You have a large data-mismatch problem because your model does a lot better on the training-dev set than on the dev set.
Explanation:
High training error compared to human-level performance suggests high bias. Also, discrepancies between the training-dev set and dev set indicate a data mismatch problem.
Q7: Is the friend right in saying the training data distribution is easier than the dev/test distribution?
Answer: There’s insufficient information to tell if your friend is right or wrong.
Explanation: While there’s a performance gap between training and dev/test, more data and a deeper analysis would be required to definitively assess the training data’s difficulty compared to the dev/test set.
Q8: Should the team prioritize adding more foggy images to the training set?
Answer: False because it depends on how easy it is to add foggy data. If foggy data is very hard and costly to collect, it might not be worth the team’s effort.
Explanation: While foggy images are a significant source of error, prioritizing them depends on the ease of collecting such data. If it’s too costly, the team should focus on the least costly sources of error first.
Q9: How much will the windshield wiper improve performance based on the dev set error of 2.2% for rain-related mistakes?
Answer: 2.2% would be a reasonable estimate of the maximum amount this windshield wiper could improve performance.
Explanation: The 2.2% error due to rain drops represents a reasonable upper bound for the potential improvement by addressing this issue with a windshield wiper.
Q10: How will adding synthesized foggy images help the model?
Answer: There is little risk of overfitting to the 1,000 pictures of fog so long as you are combining it with a much larger (>1,000) set of clean/non-foggy images.
Explanation: Data augmentation is a standard practice and doesn’t introduce overfitting as long as the synthesized data is part of a much larger training set of clean data.
Q11: Should you correct the incorrectly labeled data in the test set?
Answer:
- You should not correct the incorrectly labeled data in the test set, so that the dev and test sets continue to come from the same distribution.
Explanation:
The test set is meant to be an unbiased representation of real-world data. Correcting labels in the test set would change this distribution, skewing the results.
Q12: Can transfer learning help your colleague who is working on recognizing yellow traffic lights?
Answer: She should try using weights pre-trained on your dataset, and fine-tuning further with the yellow-light dataset.
Explanation: Transfer learning can help by leveraging the pre-trained weights on your dataset and fine-tuning on the new yellow-light dataset, which is a common practice when dealing with limited data.
Q13: How can you help your colleague working with microphones placed outside the car to hear vehicles?
Answer: Either transfer learning or multi-task learning could help our colleague get going faster.
Explanation: Both transfer learning (using pre-trained models) and multi-task learning (sharing knowledge across tasks) can benefit your colleague by leveraging the audio and vision data together.
Q14: Is Approach B more of an end-to-end approach?
Answer: False
Explanation: Approach B (detecting the traffic light first and then determining its color) is a two-step process, not an end-to-end approach, as the problem is broken down into distinct sub-tasks.
Q15: Approach A tends to be more promising than approach B if you have a __ (fill in the blank).
Answer: Large training set
Explanation: Approach A benefits from large datasets because it directly learns the mapping from images to labels, making it suitable for large training sets with complex relationships.
Frequently Asked Questions (FAQ)
Are the Structuring Machine Learning Projects Coursera quiz answers accurate?
Yes, these answers have been carefully reviewed to ensure they align with the latest course content and the best practices for structuring machine learning projects.
Can I use these answers for both practice and graded quizzes?
Absolutely! These answers are applicable for both practice quizzes and graded assessments, helping you prepare thoroughly for all evaluations.
Does this guide include answers for all modules of the course?
Yes, this guide covers answers for every module in the course, providing comprehensive coverage of the entire course content.
Will this guide help me understand how to manage machine learning projects better?
Yes, beyond providing quiz answers, this guide reinforces key concepts such as project planning, model iteration, and scaling machine learning solutions effectively.
Conclusion
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