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The Kaggle Book

You're reading from   The Kaggle Book Data analysis and machine learning for competitive data science

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801817479
Length 534 pages
Edition 1st Edition
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
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Toc

Table of Contents (20) Chapters Close

Preface
1. Part I: Introduction to Competitions
2. Introducing Kaggle and Other Data Science Competitions FREE CHAPTER 3. Organizing Data with Datasets 4. Working and Learning with Kaggle Notebooks 5. Leveraging Discussion Forums 6. Part II: Sharpening Your Skills for Competitions
7. Competition Tasks and Metrics 8. Designing Good Validation 9. Modeling for Tabular Competitions 10. Hyperparameter Optimization 11. Ensembling with Blending and Stacking Solutions 12. Modeling for Computer Vision 13. Modeling for NLP 14. Simulation and Optimization Competitions 15. Part III: Leveraging Competitions for Your Career
16. Creating Your Portfolio of Projects and Ideas 17. Finding New Professional Opportunities 18. Other Books You May Enjoy
19. Index

Modeling for Computer Vision

Computer vision tasks are among the most popular problems in practical applications of machine learning; they were the gateway into deep learning for many Kagglers, including yours truly (Konrad, that is). Over the last few years, there has been tremendous progress in the field and new SOTA libraries continue to be released. In this chapter, we will give you an overview of the most popular competition types in computer vision:

  • Image classification
  • Object detection
  • Image segmentation

We will begin with a short section on image augmentation, a group of task-agnostic techniques that can be applied to different problems to increase the generalization capability of our models.

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