کتاب دانلود کتب لاتین علوم حیاتبیوانفورماتیک
الگوریتم های ژنتیکی عملی با پایتون: استفاده از الگوریتم های ژنتیک برای حل مشکلات یادگیری عمیق و هوش مصنوعی در دنیای واقعی
الگوریتم های ژنتیکی عملی با پایتون: استفاده از الگوریتم های ژنتیک برای حل مشکلات یادگیری عمیق و هوش مصنوعی در دنیای واقعی

Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems
نویسنده: Eyal Wirsansky
سال انتشار: 2020
تعداد صفحات: 334
زبان فایل: انگلیسی
فرمت فایل: pdf
حجم فایل: 9MB
رمز فایل: www.ketabdownload.com

دانلود رایگان

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features      Explore the ins and outs of genetic algorithms with this fast-paced guide     Implement tasks such as feature selection, search optimization, and cluster analysis using Python     Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications  Book Description  Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence.  After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications.  By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learn      Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications     Use genetic algorithms to optimize functions and solve planning and scheduling problems     Enhance the performance of machine learning models and optimize deep learning network architecture     Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym     Explore how images can be reconstructed using a set of semi-transparent shapes     Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization  Who this book is for  This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book. Table of Contents      An Introduction to Genetic Algorithms     Understanding the Key Components of Genetic Algorithms     Using the DEAP Framework     Combinatorial Optimization     Constraint Satisfaction     Optimizing Continuous Functions     Enhancing Machine Learning Models Using Feature Selection     Hyperparameter Tuning Machine Learning Models     Architecture Optimization of Deep Learning Networks     Reinforcement Learning with Genetic Algorithms     Genetic Image Reconstruction     Other Evolutionary and Bio-Inspired Computation Techniques.