July 2024
Intermediate to advanced
418 pages
10h 25m
English
Overview
Dive into the fascinating world of genetic algorithms with 'Hands-On Genetic Algorithms with Python.' This book equips you with a thorough understanding of genetic algorithms paired with practical Python applications. You'll learn to solve complex optimization issues and implement bio-inspired techniques such as cellular automata, Particle Swarm Optimization, and NEAT algorithms.
What this Book will help me do
- Master the implementation of genetic algorithms using popular Python libraries such as DEAP and NumPy.
- Develop optimization models applicable to real-world problems like scheduling and AI improvements.
- Integrate genetic algorithms into machine learning workflows to enhance model performances.
- Understand and leverage cloud computing for improving algorithm efficiency and scalability.
- Gain practical expertise through hands-on projects like image reconstruction and optimization strategies.
Author(s)
Eyal Wirsansky, a senior data scientist and AI researcher, has over two decades of experience in the field. His expertise focuses on neural networks and genetic algorithms, contributing to novel approaches in artificial intelligence. Eyal's tutorial-driven style makes complex topics accessible and engaging for learners.
Who is it for?
This book is designed for software developers, engineers, or data scientists who are familiar with Python programming and are eager to venture into the world of algorithmic problem solving. Especially suitable for those looking to enhance their AI knowledge and learn practical optimization techniques for real-world applications.
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