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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

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781801814430

Length 402 pages

Edition 1st Edition

Tools

Gitlab

Concepts

Data Science

Author (1):

Angelica Lo Duca

View More author details

Table of Contents (16) Chapters

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

Summary

We have just completed the journey to build a deep learning model in TensorFlow and track it in Comet!

Throughout this chapter, we described some general concepts regarding deep learning as well as the main structure of the TensorFlow package, and some related concepts, including how to load a dataset and build and train a model in TensorFlow.

In the last part of the chapter, we implemented a practical use case that showed you how to track a deep learning experiment in Comet as well as how to build a report with the results of the experiment.

In the next chapter, we will review the basic concepts related to time series analysis and how to perform it in Comet.

The rest of the chapter is locked

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Authors (1)

Angelica Lo Duca

Angelica Lo Duca

Angelica Lo Duca is a researcher at the Institute of Informatics and Telematics of the National Research Council, Italy. She is also an external professor of Data Journalism at the University of Pisa. Her research includes Data Science, Data Journalism, and Web Applications. She used to work on Network Security, Semantic Web, Linked Data and Blockchain. She has published more than 40 scientific papers at national and international conferences and journals, and participated in many international projects and events, including as a member of the Program Committee.

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