Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorith
暫譯: 使用 Python 學習遺傳演算法：提升機器學習和 AI 模型的性能，利用強大的搜尋演算法能力
Gridin, Ivan
- 出版商: Bpb Publications
- 出版日期: 2021-02-13
- 售價: $1,080
- 貴賓價: 9.5 折 $1,026
- 語言: 英文
- 頁數: 270
- 裝訂: Quality Paper - also called trade paper
- ISBN: 8194837758
- ISBN-13: 9788194837756
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相關分類:
Python、程式語言、人工智慧、Machine Learning、Algorithms-data-structures
海外代購書籍(需單獨結帳)
相關主題
商品描述
Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions Key FeaturesComplete coverage on practical implementation of genetic algorithms.
Intuitive explanations and visualizations supply theoretical concepts.
Added examples and use-cases on the performance of genetic algorithms.
Use of Python libraries and a niche coverage on the performance optimization of genetic algorithms. Description
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. What you will learn
Understand the mechanism of genetic algorithms using popular python libraries.
Learn the principles and architecture of genetic algorithms.
Apply and Solve planning, scheduling and analytics problems in Enterprise applications.
Expert learning on prime concepts like Selection, Mutation and Crossover. Who this book is for
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. Table of Contents
1. Introduction
2. Genetic Algorithm Flow
3. Selection
4. Crossover
5. Mutation
6. Effectiveness
7. Parameter Tuning
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 About the Author
Ivan Gridin 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. 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. He is a loving father, husband, and collector of old math books. LinkedIn Profile: www.linkedin.com/in/survex
Blog links: https: //www.facebook.com/ivan.gridin/
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. What you will learn
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. Table of Contents
1. Introduction
2. Genetic Algorithm Flow
3. Selection
4. Crossover
5. Mutation
6. Effectiveness
7. Parameter Tuning
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 About the Author
Ivan Gridin 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. 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. He is a loving father, husband, and collector of old math books. LinkedIn Profile: www.linkedin.com/in/survex
Blog links: https: //www.facebook.com/ivan.gridin/
商品描述(中文翻譯)
為您的 AI 模型和機器學習應用提供高品質的優化和搜尋解決方案
主要特點書籍描述
遺傳演算法是機器學習中最簡單且最強大的技術之一。本書《使用 Python 學習遺傳演算法》指導讀者從遺傳演算法的基本概念開始，直到在生產環境中的實際應用。
每一章都為讀者提供對每個概念的直觀理解。您將學習如何從零開始構建遺傳演算法並將其應用於現實問題。通過實用的插圖範例，您將學會設計和選擇最適合特定任務的模型架構。通過尖端範例，如雷達和足球經理問題陳述，您將學會如何解決高維大數據挑戰，並優化遺傳演算法。
您將學到什麼
本書適合誰
本書適合數據科學團隊、分析團隊、AI 工程師和希望將遺傳演算法整合到其機器學習和 AI 應用中的專業人士。雖然不需要特別的機器學習專業知識，但期望具備基本的 Python 知識。
目錄
1. 介紹
2. 遺傳演算法流程
3. 選擇
4. 交叉
5. 突變
6. 效能
7. 參數調整
8. 黑箱函數
9. 組合優化：二進位基因編碼
10. 組合優化：有序基因編碼
11. 其他常見問題
12. 自適應遺傳演算法
13. 提升性能
關於作者
伊凡·格里丁是一位數學家、全端開發者、數據科學家和機器學習專家，居住在俄羅斯莫斯科。多年來，他在分佈式高負載系統上工作，並在實踐中實施了不同的機器學習方法。他研究的關鍵領域之一是預測時間序列模型的設計和分析。 伊凡在概率論、隨機過程理論、時間序列分析、機器學習、深度學習和優化方面擁有基本的數學技能。他對 Java、Python、PHP 和 MATLAB 等各種程式語言也有深入的知識和理解。 他是一位慈愛的父親、丈夫和舊數學書籍的收藏家。
LinkedIn 個人檔案: www.linkedin.com/in/survex
部落格連結: https://www.facebook.com/ivan.gridin/
