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Hands-On Genetic Algorithms with Python

You're reading from   Hands-On Genetic Algorithms with Python Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Length 346 pages
Edition 1st Edition
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Author (1):
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Eyal Wirsansky Eyal Wirsansky
Author Profile Icon Eyal Wirsansky
Eyal Wirsansky
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics of Genetic Algorithms
2. An Introduction to Genetic Algorithms FREE CHAPTER 3. Understanding the Key Components of Genetic Algorithms 4. Section 2: Solving Problems with Genetic Algorithms
5. Using the DEAP Framework 6. Combinatorial Optimization 7. Constraint Satisfaction 8. Optimizing Continuous Functions 9. Section 3: Artificial Intelligence Applications of Genetic Algorithms
10. Enhancing Machine Learning Models Using Feature Selection 11. Hyperparameter Tuning of Machine Learning Models 12. Architecture Optimization of Deep Learning Networks 13. Reinforcement Learning with Genetic Algorithms 14. Section 4: Related Technologies
15. Genetic Image Reconstruction 16. Other Evolutionary and Bio-Inspired Computation Techniques 17. Other Books You May Enjoy

Image processing in Python

To achieve our goal, we will need to carry out various image processing operations; for example, we will need to create an image from scratch, draw shapes onto an image, plot an image, open an image file, save an image to a file, compare two images, and possibly resize an image. In the following sections, we will explore some of the ways these operations can be performed when using Python.

Python image processing libraries

Out of the wealth of image processing libraries available for Python programmers, we chose to use three of the most prominent ones. These libraries will be briefly discussed in the following subsections.

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