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Title : Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

Author : Eyal Wirsansky

category : Books,Computers & Technology,Computer Science

Publisher : Eyal Wirsansky

ISBN-10 : 1838557741

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Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems by Eyal Wirsansky

Read Online and Download Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems by Eyal Wirsansky. 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

Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems by Eyal Wirsansky Review

Starts with an overview of genetic algorithms (GA) then introduces the DEAP framework for evolutionary computation, which is used in the book for the various example problems/solutions. All of the code is explained and as you read about the examples and inspect the code, you can get ideas for possible applications of GA to real problems. Very gentle learning curve, but you should know some basic Python before starting this book.

I wanted break into GA and apply it to real-world applications as quickly as possible. This book is the very book I was looking for. It breaks down and explains complicated concepts clearly and concisely. The sample code is easy to follow and understand. There are no difficult maths or even challenging sentences. Unlike many other books on the field, it doesn't try to pile up words with windy content. This book is concise, straightforward, and well written. From an engineer's eyes, it delivered the information in the most efficient way. Also to my pleasant surprise, this book covers related topics like PSO and ALift that I didn't expect. It doesn't talk about these topics extensively but gives me critical pointers for further learning.This book is ideal for someone who is:- new to GA.- already experienced with Python.- constrained by time.- looking for a broad coverage on this topic.It's not a good fit for someone who is:- unfamiliar with Python.- looking for theories or rigorous maths.- looking for in-depth knowledge (although this book links to in-depths materials).

The book doesn't get to even the first snippet of code until the third chapter. It starts with a pretty thorough introduction to Genetic Algorithms that covers the whole first section. I would expect a "hands-on" book to introduce these topics along side code samples. What samples the book does include are often one or two lines out of context without much explanation for how to use them. The Kindle formatting is also off so each numbered entry in a list of paragraphs is indented. This leads to pages that look like the attached photo that make reading the book more of a chore than a pleasure.

This was the best Genetic Algorithm book ever in my life. Due to its simplicity and pesudo-code-like nature of the Python language, the example codes does not interfere with the readers’ intellectual engagement into the beauty of evolutionary algorithms.

The code examples were easy to follow. The author did a nice job of explaining a complex concept in understandable terms. I really enjoyed the book. It helped me gain a better understanding of the use of genetic algorithms.

Hands-On Genetic AlgorithmsYour browser indicates if you've visited this linkhttps cloudflare-ipfs com/ipfs/bafykbzaceapxmi6w7zql6fehm75rz6tkmd2rlgenoerzjzsjmasybto5kky3g?filename= - Hands-On Genetic Algorithms with Python_ Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems-Packt Publishing (2020) pdffocused his research on genetic algorithms and neural networks One outcome of his research is a novel supervised machine learning algorithm that combines the two Eyal leads the Jacksonville (FL) Java user group, hosts the Artificial Intelligence for Enterprise virtual user group, and writes the developer-oriented artificial intelligence blog Hands-On Genetic Algorithms with Python: Applying genetic Your browser indicates if you've visited this linkhttps com/Hands-Genetic-Algorithms-Python-intelligence/dp/1838557741Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems [Wirsansky, Eyal] on com *FREE* shipping on qualifying offers Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problemsHands-On Genetic Algorithms with Python by Wirsansky, Eyal Your browser indicates if you've visited this linkhttps ebooks com/en-us/book/209940865/hands-on-genetic-algorithms-with-python/eyal-wirsansky/Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems by 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 Hands-On Genetic Algorithms with Python: Applying genetic Your browser indicates if you've visited this linkhttps paradedaily com/computers-technology/hands-on-genetic-algorithms-with-python-applying-genetic-algorithms-to-solve-real-world-deep-learning-and-artificial-intelligence-problems-pdf-free-download/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 guideHands-On Genetic Algorithms with Python: Applying genetic Your browser indicates if you've visited this linkhttps dokumen pub/hands-on-genetic-algorithms-with-python-applying-genetic-algorithms-to-solve-real-world-deep-learning-and-artificial-intelligence-problems-1nbsped-1838557741-978-1838557744 htmlHands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems [1 ed ] 1838557741, 978-1838557744 Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve ma 2,646 913 9MB English Pages 309 [334] Year 2020Hands-On Genetic Algorithms with Python - Free PDF DownloadYour browser indicates if you've visited this linkhttps wowebook org/hands-on-genetic-algorithms-with-python/Hands-On Genetic Algorithms with Python: 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 Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the Hands-On Genetic Algorithms with Python - Free PDF DownloadYour browser indicates if you've visited this linkhttps ebook3000 co/hands-on-genetic-algorithms-with-python/eBook Details: Paperback: 346 pages Publisher: WOW! eBook (February 11, 2020) Language: English ISBN-10: 1838557741 ISBN-13: 978-1838557744 eBook Description: Hands-On Genetic Algorithms with Python: 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 Hands-On-Genetic-Algorithms-with-Python - GitHubYour browser indicates if you've visited this linkhttps github com/PacktPublishing/Hands-On-Genetic-Algorithms-with-PythonHands-On-Genetic-Algorithms-with-Python Hands-On Genetic Algorithms with Python, Published by PacktHands-On Genetic Algorithms with Python (Applying genetic Your browser indicates if you've visited this linkhttps ridibooks com/books/3210000976 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 Book Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution Hands On Genetic Algorithms With Python / TavazSearchYour browser indicates if you've visited this linkhttps tavaz xyz/Hands-On-Genetic-Algorithms-with-Python/article_129067 aspHands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems by English | 2020 | ISBN: 1838557741 | 334 Pages | PDF,EPUB | 15 MBMore results

Hands-On Genetic Algorithms with Python: Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems by Eyal Wirsansky

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