January 2020
Intermediate to advanced
346 pages
9h 8m
English
Overview
This book will introduce you to genetic algorithms and how their principles can be effectively applied using Python to tackle real-world artificial intelligence and optimization problems. Through practical examples and detailed explanations, you'll learn to solve complex challenges such as function optimization, machine learning enhancement, and innovative applications like reinforcement learning and image reconstruction.
What this Book will help me do
- Implement genetic algorithms using Python to solve practical AI problems.
- Optimize deep learning and machine learning models with genetic algorithms.
- Utilize evolutionary algorithms for planning, scheduling, and combinatorial challenges.
- Enhance AI systems through reinforcement learning approaches using genetic concepts.
- Explore advanced topics like genetic programming and bio-inspired computing techniques.
Author(s)
Eyal Wirsansky is an experienced software developer and AI practitioner, specializing in evolutionary and genetic algorithms. With a strong focus on practical implementation, Eyal aims to provide readers with efficient and innovative solutions to computational challenges. His approachable and engaging writing style helps simplify complex concepts for aspiring developers and AI enthusiasts.
Who is it for?
This book is ideal for software developers, data scientists, and AI enthusiasts with a basic understanding of Python and foundational concepts in mathematics and computer science. It is particularly suited for those looking to apply optimization techniques and evolutionary algorithms to solve real-world problems. If you're interested in enhancing your AI and machine learning skills with genetic algorithms, this book is for you.
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