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Comet for Data Science

You're reading from   Comet for Data Science Enhance your ability to manage and optimize the life cycle of your data science project

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801814430
Length 402 pages
Edition 1st Edition
Tools
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Author (1):
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Angelica Lo Duca Angelica Lo Duca
Author Profile Icon Angelica Lo Duca
Angelica Lo Duca
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1 – Getting Started with Comet
2. Chapter 1: An Overview of Comet FREE CHAPTER 3. Chapter 2: Exploratory Data Analysis in Comet 4. Chapter 3: Model Evaluation in Comet 5. Section 2 – A Deep Dive into Comet
6. Chapter 4: Workspaces, Projects, Experiments, and Models 7. Chapter 5: Building a Narrative in Comet 8. Chapter 6: Integrating Comet into DevOps 9. Chapter 7: Extending the GitLab DevOps Platform with Comet 10. Section 3 – Examples and Use Cases
11. Chapter 8: Comet for Machine Learning 12. Chapter 9: Comet for Natural Language Processing 13. Chapter 10: Comet for Deep Learning 14. Chapter 11: Comet for Time Series Analysis 15. Other Books You May Enjoy

Section 3 – Examples and Use Cases

In this final section, you will learn how to use Comet for model building. You will focus on four different types of models, depending on either the specific technology you are using or the different tasks you want to solve. You will learn how to use Comet to build models for machine learning (Chapter 8, Comet for Machine Learning), natural language processing (Chapter 9, Comet for Natural Language Processing), deep learning (Chapter 10, Comet for Deep Learning), and time series analysis (Chapter 11, Comet for Time Series Analysis).

In each chapter of this section, you will see an overview of the considered technology, a description of a Python library that implements that technology, and finally, a practical example, which describes step by step how to combine Comet with a specific technology.

The main focus of this section is to provide you with practical examples that you can use as guidelines for your future data science projects...

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