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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

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781800567887

Length 270 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Dario Radečić

View More author details

Table of Contents (14) Chapters

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

Q&A

Define the term "parallelism."
Explain which types of tasks can and can't be parallelized.
List and explain three options for implementing parallelism in your applications (all are listed in this chapter).
What is Dask and what makes it superior to NumPy and pandas for larger datasets?
Name and explain three basic data structures that are implemented in Dask.
What is a Dask cluster?
What do you have to do to tell TPOT it should use Dask for training?

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Authors (1)

Radečić

Radečić

Dario Radečić is a full-time data scientist at Neos, in Croatia, a part-time data storyteller at Appsilon, in Poland, and a business owner. Dario has a master's degree in data science and years of experience in data science and machine learning, with an emphasis on automated machine learning. He is also a top writer in artificial intelligence on Medium and the owner of a data science blog called Better Data Science.

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