{"product_id":"learning-genetic-algorithms-with-python-empower-the-performance-of-machine-learning-and-ai-models-with-the-capabilities-of-a-powerful-search-algorith-paperback","title":"Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorith - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eIvan Gridin\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRefuel your AI Models and ML applications with High-Quality Optimization and Search Solutions\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e \u003cb\u003eKey Features\u003c\/b\u003e\u003cli\u003eComplete coverage on practical implementation of genetic algorithms.\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003eIntuitive explanations and visualizations supply theoretical concepts.\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003eAdded examples and use-cases on the performance of genetic algorithms.\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003eUse of Python libraries and a niche coverage on the performance optimization of genetic algorithms. \u003cp\u003e\u003c\/p\u003e\n\u003cb\u003eDescription\u003c\/b\u003e\u003cbr\u003eGenetic 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.\u003cbr\u003e 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. \u003cp\u003e\u003c\/p\u003e\n\u003cb\u003eWhat you will learn\u003c\/b\u003e\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003eUnderstand the mechanism of genetic algorithms using popular python libraries.\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003eLearn the principles and architecture of genetic algorithms.\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003eApply and Solve planning, scheduling and analytics problems in Enterprise applications.\u003cbr\u003e\n\u003c\/li\u003e\u003cli\u003e Expert learning on prime concepts like Selection, Mutation and Crossover. \u003cp\u003e\u003c\/p\u003e\n\u003cb\u003eWho this book is for\u003c\/b\u003e\u003cbr\u003eThe 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. \u003cp\u003e\u003c\/p\u003e\n\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction\u003cbr\u003e2. Genetic Algorithm Flow\u003cbr\u003e3. Selection\u003cbr\u003e4. Crossover\u003cbr\u003e5. Mutation\u003cbr\u003e6. Effectiveness\u003cbr\u003e7. Parameter Tuning\u003cbr\u003e8. Black-box Function\u003cbr\u003e9. Combinatorial Optimization: Binary Gene Encoding\u003cbr\u003e10. Combinatorial Optimization: Ordered Gene Encoding\u003cbr\u003e11. Other Common Problems\u003cbr\u003e12. Adaptive Genetic Algorithm\u003cbr\u003e13. Improving Performance \u003cp\u003e\u003c\/p\u003e\n\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e \u003cb\u003e Ivan Gridin\u003c\/b\u003e is a mathematician, fullstack developer, data scientist, and machine learning expert living in Moscow, Russia. Over the years, he worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the key areas of his research is design and analysis of predictive time series models. \u003cp\u003e\u003c\/p\u003e Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization. He also has an in-depth knowledge and understanding of various programming languages such as Java, Python, PHP, and MATLAB. \u003cp\u003e\u003c\/p\u003eHe is a loving father, husband, and collector of old math books. \u003cp\u003e\u003c\/p\u003e\n\u003cb\u003eLinkedIn Profile\u003c\/b\u003e: www.linkedin.com\/in\/survex\u003cbr\u003e\u003cb\u003eBlog links\u003c\/b\u003e: https: \/\/www.facebook.com\/ivan.gridin\/\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 270\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.57 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 13, 2021\u003c\/div\u003e\n            \u003c\/li\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43152704766015,"sku":"9788194837756","price":40.42,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/brkhCiian79788194837756_16652cfe-19a8-43d0-9475-a58a2f650335.jpg?v=1776939997","url":"https:\/\/dhlswag.com\/products\/learning-genetic-algorithms-with-python-empower-the-performance-of-machine-learning-and-ai-models-with-the-capabilities-of-a-powerful-search-algorith-paperback","provider":"BBB","version":"1.0","type":"link"}