Empower the performance of Machine Learning and AI models with the capabilities of a powerful search
Updated on Mar, 2021
Programming, Data Science,
Formats : PDF, EPUB (Downlodable)
ISBN : 9788194837756
Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions
Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book ‘Learning Genetic Algorithms with Python’ guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments.
Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms.
The book is for Data Science team, Analytics team, AI Engineers, ML Professionals who want to integrate genetic algorithms to refuel their ML and AI applications. No special expertise about machine learning is required although a basic knowledge of Python is expected.
2. Genetic Algorithm Flow
8. Black-box Function
9. Combinatorial Optimization: Binary Gene Encoding
10. Combinatorial Optimization: Ordered Gene Encoding
11. Other Common Problems
12. Adaptive Genetic Algorithm
13. Improving Performance
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