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Machine Learning Automation with TPOT

You're reading from   Machine Learning Automation with TPOT Build, validate, and deploy fully automated machine learning models with Python

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800567887
Length 270 pages
Edition 1st Edition
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Author (1):
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Dario Radečić Dario Radečić
Author Profile Icon Dario Radečić
Dario Radečić
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Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Introducing Machine Learning and the Idea of Automation
2. Chapter 1: Machine Learning and the Idea of Automation FREE CHAPTER 3. Section 2: TPOT – Practical Classification and Regression
4. Chapter 2: Deep Dive into TPOT 5. Chapter 3: Exploring Regression with TPOT 6. Chapter 4: Exploring Classification with TPOT 7. Chapter 5: Parallel Training with TPOT and Dask 8. Section 3: Advanced Examples and Neural Networks in TPOT
9. Chapter 6: Getting Started with Deep Learning: Crash Course in Neural Networks 10. Chapter 7: Neural Network Classifier with TPOT 11. Chapter 8: TPOT Model Deployment 12. Chapter 9: Using the Deployed TPOT Model in Production 13. Other Books You May Enjoy

Chapter 5: Parallel Training with TPOT and Dask

In this chapter, you'll dive into a bit of a more advanced topic; that is, automated machine learning. You'll learn how to handle machine learning tasks in a parallel manner by distributing the work on a Dask cluster. This chapter will be more theoretical than the previous two, but you will still learn many useful things.

We'll cover essential topics and ideas behind parallelism in Python, and you'll learn how to achieve parallelism in a couple of different ways. Then, we'll dive deep into the Dask library, explore its basic functionality, and see how you can tie it with TPOT.

This chapter will cover the following topics:

  • Introduction to parallelism in Python
  • Introduction to the Dask library
  • Training machine learning models with TPOT and Dask

Let's get started!

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