Book Image

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
5 (2)
Book Image

The Kaggle Book

5 (2)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
Table of Contents (20 chapters)
Preface
1
Part I: Introduction to Competitions
6
Part II: Sharpening Your Skills for Competitions
15
Part III: Leveraging Competitions for Your Career
18
Other Books You May Enjoy
19
Index

Working and Learning with Kaggle Notebooks

Kaggle Notebooks – which until recently were called Kernels – are Jupyter Notebooks in the browser that can run free of charge. This means you can execute your experiments from any device with an internet connection, although something bigger than a mobile phone is probably a good idea. The technical specification of the environment (as of the time of this writing) is quoted below from the Kaggle website; the most recent version can be verified at https://www.kaggle.com/docs/notebooks:

  • 12 hours execution time for CPU/GPU, 9 hours for TPU
  • 20 gigabytes of auto-saved disk space (/kaggle/working)
  • Additional scratchpad disk space (outside /kaggle/working) that will not be saved outside of the current session

CPU specifications:

  • 4 CPU cores
  • 16 gigabytes of RAM

GPU specifications:

  • 2 CPU cores
  • 13 gigabytes of RAM

TPU specifications:

  • 4 CPU...