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Deep Learning with fastai Cookbook

You're reading from   Deep Learning with fastai Cookbook Leverage the easy-to-use fastai framework to unlock the power of deep learning

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781800208100
Length 340 pages
Edition 1st Edition
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Author (1):
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Mark Ryan Mark Ryan
Author Profile Icon Mark Ryan
Mark Ryan
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Table of Contents (10) Chapters Close

Preface 1. Chapter 1: Getting Started with fastai 2. Chapter 2: Exploring and Cleaning Up Data with fastai FREE CHAPTER 3. Chapter 3: Training Models with Tabular Data 4. Chapter 4: Training Models with Text Data 5. Chapter 5: Training Recommender Systems 6. Chapter 6: Training Models with Visual Data 7. Chapter 7: Deployment and Model Maintenance 8. Chapter 8: Extended fastai and Deployment Features 9. Other Books You May Enjoy

Test your knowledge

In this chapter, we have reviewed a broad range of topics, from taking full advantage of the information that fastai provides about models to making your web deployments available to users outside of your local system. In this section, you will get the opportunity to exercise some of the concepts you learned about in this chapter.

Explore the value of repeatable results

In the Using callbacks to get the most out of your training cycle recipe, you made a call to the set_seed() function prior to training each of the models. In that recipe, I stated that these calls were necessary to ensure repeatable results for multiple training cycles. Test out this assertion yourself by following these steps:

  1. First, make a copy of the training_with_tabular_datasets_callbacks.ipynb notebook.
  2. Update your new notebook by commenting out the first call to set_seed() and rerun the whole notebook. What differences do you see in the output of fit_one_cycle() between the...
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