You're reading from Hands-On Genetic Algorithms with Python Apply genetic algorithms to solve real-world AI and machine learning problems

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781805123798

Length 418 pages

Edition 2nd Edition

Languages

Python

Tools

Jupyter

Concepts

Data Science

Author (1):

Eyal Wirsansky

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Table of Contents (24) Chapters

Preface

1. Part 1: The Basics of Genetic Algorithms

2. Chapter 1: An Introduction to Genetic Algorithms FREE CHAPTER

3. Chapter 2: Understanding the Key Components of Genetic Algorithms

4. Part 2: Solving Problems with Genetic Algorithms

5. Chapter 3: Using the DEAP Framework

6. Chapter 4: Combinatorial Optimization

7. Chapter 5: Constraint Satisfaction

8. Chapter 6: Optimizing Continuous Functions

9. Part 3: Artificial Intelligence Applications of Genetic Algorithms

10. Chapter 7: Enhancing Machine Learning Models Using Feature Selection

11. Chapter 8: Hyperparameter Tuning of Machine Learning Models

12. Chapter 9: Architecture Optimization of Deep Learning Networks

13. Chapter 10: Reinforcement Learning with Genetic Algorithms

14. Chapter 11: Natural Language Processing

15. Chapter 12: Explainable AI, Causality, and Counterfactuals with Genetic Algorithms

16. Part 4: Enhancing Performance with Concurrency and Cloud Strategies

17. Chapter 13: Accelerating Genetic Algorithms – the Power of Concurrency

18. Chapter 14: Beyond Local Resources – Scaling Genetic Algorithms in the Cloud

19. Part 5: Related Technologies

20. Chapter 15: Evolutionary Image Reconstruction with Genetic Algorithms

21. Chapter 16: Other Evolutionary and Bio-Inspired Computation Techniques

22. Index

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Who this book is for

This book is for data scientists, software developers, and AI enthusiasts eager to break into genetic algorithms and apply them to real-world, intelligent applications as quickly as possible. It’s crafted for those who wish to master genetic algorithms swiftly and effectively.

The primary audience for this book comprises three distinct groups:

Data scientists: This book is a treasure trove for data scientists looking to integrate genetic algorithms into their toolbox. You will learn how to apply these algorithms to complex data problems, enhancing your predictive models and analytical capabilities.
Software developers: As a software developer, this book serves as a gateway to integrating genetic algorithms into software solutions. Whether you’re involved in developing AI applications, optimization tools, or complex system simulations, understanding how to implement and fine-tune genetic algorithms can significantly enhance the functionality and efficiency of your software. The book offers real-world examples and step-by-step guides, helping you to seamlessly incorporate these algorithms into your projects.
AI enthusiasts: If you are passionate about AI and keen on expanding your knowledge in this domain, this book will be particularly beneficial. You will get a thorough grounding in genetic algorithms, an essential part of AI, and a hands-on approach to applying them in various AI-driven applications. The skills and knowledge acquired will be a significant stepping stone in your journey toward becoming an AI expert.