For more questions, including detailed answers and links to the video timeline, have a look at Radek Osmulski’s .

      • Lots of math T / F
      • Lots of data T / F
      • Lots of expensive computers T / F
      • A PhD T / F

    2. Name five areas where deep learning is now the best in the world.

    4. Based on the book of the same name, what are the requirements for parallel distributed processing (PDP)?
    5. What were the two theoretical misunderstandings that held back the field of neural networks?
    6. What is a GPU?
    7. Open a notebook and execute a cell containing: . What happens?
    8. Complete the Jupyter Notebook online appendix.
    9. Why is it hard to use a traditional computer program to recognize images in a photo?
    10. What did Samuel mean by “weight assignment”?
    11. What term do we normally use in deep learning for what Samuel called “weights”?
    12. Draw a picture that summarizes Samuel’s view of a machine learning model.
    13. Why is it hard to understand why a deep learning model makes a particular prediction?
    14. What is the name of the theorem that shows that a neural network can solve any mathematical problem to any level of accuracy?
    15. What do you need in order to train a model?
    16. How could a feedback loop impact the rollout of a predictive policing model?
    17. Do we always have to use 224×224-pixel images with the cat recognition model?
    18. What is the difference between classification and regression?
    19. What is a validation set? What is a test set? Why do we need them?
    20. Can we always use a random sample for a validation set? Why or why not?
    21. What is overfitting? Provide an example.
    22. What is a metric? How does it differ from “loss”?
    23. How can pretrained models help?
    24. What is the “head” of a model?
    25. What kinds of features do the early layers of a CNN find? How about the later layers?
    26. Are image models only useful for photos?
    27. What is an “architecture”?
    28. What is segmentation?
    29. What is y_range used for? When do we need it?
    30. What are “hyperparameters”?
    31. What’s the best way to avoid failures when using AI in an organization?

    Each chapter also has a “Further Research” section that poses questions that aren’t fully answered in the text, or gives more advanced assignments. Answers to these questions aren’t on the book’s website; you’ll need to do your own research!

    1. Try to think of three areas where feedback loops might impact the use of machine learning. See if you can find documented examples of that happening in practice.