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)
Part I: Introduction to Competitions
Part II: Sharpening Your Skills for Competitions
Part III: Leveraging Competitions for Your Career
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Semantic segmentation

The easiest way to think about segmentation is that it classifies each pixel in an image, assigning it to a corresponding class; combined, those pixels form areas of interest, such as regions with disease on an organ in medical images. By contrast, object detection (discussed in the previous section) classifies patches of an image into different object classes and creates bounding boxes around them.

We will demonstrate the modeling approach using data from the Sartorius – Cell Instance Segmentation competition ( In this one, the participants were tasked to train models for instance segmentation of neural cells using a set of microscopy images.

Our solution will be built around Detectron2, a library created by Facebook AI Research that supports multiple detection and segmentation algorithms.

Detectron2 is a successor to the original Detectron library (