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Generative Adversarial Learning: Architectures and Applications (Intelligent Systems Reference Library #217)
by Vasile Palade Roozbeh Razavi-Far Ariel Ruiz-Garcia Juergen SchmidhuberThis book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
Generative Adversarial Networks and Deep Learning: Theory and Applications
by Roshani RautThis book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc. Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum
Generative Adversarial Networks Cookbook
by Josh KalinThis book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.
Generative Adversarial Networks for Image Generation
by Xudong Mao Qing LiGenerative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.
Generative Adversarial Networks in Practice
by Mehdi GhayoumiThis book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key Features: • Guides you through the complex world of GANs, demystifying their intricacies • Accompanies your learning journey with real-world examples and practical applications • Navigates the theory behind GANs, presenting it in an accessible and comprehensive way • Simplifies the implementation of GANs using popular deep learning platforms • Introduces various GAN architectures, giving readers a broad view of their applications • Nurture your knowledge of AI with our comprehensive yet accessible content • Practice your skills with numerous case studies and coding examples • Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples • Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs • Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject • Takes you through GAN applications across different data types, highlighting their versatility • Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research • Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge • Empowers you with the skills and knowledge needed to confidently use GANs in your projects Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras
by Kailash AhirwarThis book is intended for data scientists, machine learning developers, deep learning practitioners and AI enthusiasts who want a project guide to test their knowledge and expertise in building real-world GANs models. These full-fledged projects will help you master machine learning, and neural network principles. Basic understanding of machine learning and deep learning concepts will be handy. Hands-on experience in Tensorflow or Keras will be a plus point
Generative AI: Navigating the Course to the Artificial General Intelligence Future
by Martin MusiolAn engaging and essential discussion of generative artificial intelligence In Generative AI: Navigating the Course to the Artificial General Intelligence Future, celebrated author Martin Musiol—founder and CEO of generativeAI.net and GenAI Lead for Europe at Infosys—delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space. Musiol analyzes complex and foundational topics in generative AI, breaking them down into straightforward and easy-to-understand pieces. You'll also find: Bold predictions about the future emergence of Artificial General Intelligence via the merging of current AI models Fascinating explorations of the ethical implications of AI, its potential downsides, and the possible rewards Insightful commentary on Autonomous AI Agents and how AI assistants will become integral to daily life in professional and private contexts Perfect for anyone interested in the intersection of ethics, technology, business, and society—and for entrepreneurs looking to take advantage of this tech revolution—Generative AI offers an intuitive, comprehensive discussion of this fascinating new technology.
Generative AI: How ChatGPT and Other AI Tools Will Revolutionize Business
by Tom TaulliThis book will show how generative technology works and the drivers. It will also look at the applications – showing what various startupsand large companies are doing in the space. There will also be a look at the challenges and risk factors.During the past decade, companies have spent billions on AI. But the focus has been on applying the technology to predictions – which is known as analytical AI. It can mean that you receive TikTok videos that you cannot resist. Or analytical AI can fend against spam or fraud or forecast when a package will be delivered. While such things are beneficial, there is much more to AI. The next megatrend will be leveraging the technology to be creative. For example, you could take a book and an AI model will turn it into a movie – at very little cost. This is all part of generative AI. It’s still in the nascent stages but it is progressing quickly. Generative AI can already create engaging blog posts, social media messages, beautiful artwork and compelling videos.The potential for this technology is enormous. It will be useful for many categories like sales, marketing, legal, product design, code generation, and even pharmaceutical creation.What You Will LearnThe importance of understanding generative AIThe fundamentals of the technology, like the foundation and diffusion modelsHow generative AI apps workHow generative AI will impact various categories like the law, marketing/sales, gaming, product development, and code generation.The risks, downsides and challenges.Who This Book is ForProfessionals that do not have a technical background. Rather, the audience will be mostly those in Corporate America (such as managers) as well as people in tech startups, who will need an understanding of generative AI to evaluate the solutions.
Generative AI in Higher Education: The ChatGPT Effect
by Cecilia Ka Chan Tom CollotonChan and Colloton’s book is one of the first to provide a comprehensive examination of the use and impact of ChatGPT and Generative AI (GenAI) in higher education.Since November 2022, every conversation in higher education has involved ChatGPT and its impact on all aspects of teaching and learning. The book explores the necessity of AI literacy tailored to professional contexts, assess the strengths and weaknesses of incorporating ChatGPT in curriculum design, and delve into the transformation of assessment methods in the GenAI era. The authors introduce the Six Assessment Redesign Pivotal Strategies (SARPS) and an AI Assessment Integration Framework, encouraging a learner-centric assessment model. The necessity for well-crafted AI educational policies is explored, as well as a blueprint for policy formulation in academic institutions. Technical enthusiasts are catered to with a deep dive into the mechanics behind GenAI, from the history of neural networks to the latest advances and applications of GenAI technologies.With an eye on the future of AI in education, this book will appeal to educators, students and scholars interested in the wider societal implications and the transformative role of GenAI in pedagogy and research.
Generative AI on AWS
by Chris Fregly Antje Barth Shelbee EigenbrodeCompanies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.Apply generative AI to your business use casesDetermine which generative AI models are best suited to your task Perform prompt engineering and in-context learningFine-tune generative AI models on your datasets with low-rank adaptation (LoRA)Align generative AI models to human values with reinforcement learning from human feedback (RLHF)Augment your model with retrieval-augmented generation (RAG)Explore libraries such as LangChain and ReAct to develop agents and actionsBuild generative AI applications with Amazon Bedrock
Generative AI Security: Theories and Practices (Future of Business and Finance)
by Ken Huang Yang Wang Ben Goertzel Yale Li Sean Wright Jyoti PonnapalliThis book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity. The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, and the emerging fields of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program. The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity.
Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, GPT models and more
by Raghav Bali Joseph BabcockUnderstand the theory behind deep generative models and experiment with practical examplesKey FeaturesBuild a solid understanding of the inner workings of generative modelsExperiment with practical TensorFlow 2.x implementations of state-of-the-art modelsExplore a wide range of current and emerging use cases for deep generative AIBook DescriptionDeep generative models are powerful tools that rival human creative capabilities. In this book, you'll discover how these models emerged, from restricted Boltzmann machines and deep belief networks to VAEs, GANs, and beyond. You'll develop a foundational understanding of generative AI and learn how to implement models yourself in TensorFlow, supported by references to seminal and current research. After getting to grips with the fundamentals of deep neural networks, you'll set up a scalable code lab in the cloud and begin to explore the huge breadth of potential use cases for generative models. You'll look at Open AI's news generator, networks for style transfer and deepfakes, synergy with reinforcement learning, and more. As you progress, you'll recreate the code that makes these possible, piecing together TensorFlow layers, utility functions, and training loops to uncover links between the different modes of generation. By the end of this book, you will have acquired the knowledge to create and implement your own generative AI models.What you will learnImplement paired and unpaired style transfer with networks like StyleGANUse facial landmarks, autoencoders, and pix2pix GAN to create deepfakesBuild several text generation pipelines based on LSTMs, BERT, and GPT-2, learning how attention and transformers changed the NLP landscapeCompose music using hands-on LSTM models, simple GANs, and the intricate MuseGANTrain a deep learning agent to move through a simulated physical environmentDiscover emerging applications of generative AI, such as folding proteins and creating videos from images Who this book is forThis book will appeal to Python programmers, seasoned modelers, and machine learning engineers who are keen to learn about the creation and implementation of generative models. To make the most out of this book, you should have a basic familiarity with probability theory, linear algebra, and deep learning.
Generative Art: A practical guide using Processing
by Matt PearsonSummaryGenerative Art presents both the technique and the beauty of algorithmic art. The book includes high-quality examples of generative art, along with the specific programmatic steps author and artist Matt Pearson followed to create each unique piece using the Processing programming language.About the TechnologyArtists have always explored new media, and computer-based artists are no exception. Generative art, a technique where the artist creates print or onscreen images by using computer algorithms, finds the artistic intersection of programming, computer graphics, and individual expression. The book includes a tutorial on Processing, an open source programming language and environment for people who want to create images, animations, and interactions.About the BookGenerative Art presents both the techniques and the beauty of algorithmic art. In it, you'll find dozens of high-quality examples of generative art, along with the specific steps the author followed to create each unique piece using the Processing programming language. The book includes concise tutorials for each of the technical components required to create the book's images, and it offers countless suggestions for how you can combine and reuse the various techniques to create your own works. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsideThe principles of algorithmic artA Processing language tutorialUsing organic, pseudo-random, emergent, and fractal processes=================================================Table of ContentsPart 1 Creative CodingGenerative Art: In Theory and PracticeProcessing: A Programming Language for ArtistsPart 2 Randomness and NoiseThe Wrong Way to Draw A LineThe Wrong Way to Draw a CircleAdding DimensionsPart 3 ComplexityEmergenceAutonomyFractals
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
by David FosterGenerative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos; Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation; Create recurrent generative models for text generation and learn how to improve the models using attention; Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting; Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN.
Generative Deep Learning: Teaching Machines To Paint, Write, Compose, And Play
by David FosterGenerative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.Discover how VAEs can change facial expressions in photosTrain GANs to generate images based on your own datasetBuild diffusion models to produce new varieties of flowersTrain your own GPT for text generationLearn how large language models like ChatGPT are trainedExplore state-of-the-art architectures such as StyleGAN2 and ViT-VQGANCompose polyphonic music using Transformers and MuseGANUnderstand how generative world models can solve reinforcement learning tasksDive into multimodal models such as DALL.E 2, Imagen, and Stable DiffusionThis book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
Generative KI als neues Teammitglied im Marketing: Ein Leitfaden für Marketingmanger:innen (essentials)
by Bernhard WeckeGenerative Künstliche Intelligenz bringt die digitale Transformation im Marketing auf eine neue Ebene und stellt Marketingverantwortliche vor unbekannte Herausforderungen. Erfahren Sie, wie sich die Rolle von Marketingmanager:innen in Zeiten von Generativer KI verändert und welche neuen Kompetenzen gefragt sind. Darüber hinaus gibt das Buch einen Überblick über die Auswirkungen von Generativer KI auf Marketingorganisationen und einen konkreten Leitfaden zu den Handlungsfeldern. .
Genere ingresos pasivos con quora: Y olvidese de su trabajo
by Marcus PfeifferEste libro trata sobre cómo ganar dinero bajo un sistema de recomendaciones de productos y servicios a través de la plataforma de quora, haciendo ingresos pasivos en línea.
Generic Data Structures and Algorithms in Go: An Applied Approach Using Concurrency, Genericity and Heuristics
by Richard WienerAdvance your understanding of generic data structures and algorithms and their applications using Go and the effective use of concurrency. You are invited on a journey that aims to improve your programming and problem-solving skills. This book takes you to the next step by showing how to get your programs to work efficiently as well as correctly. As you explore many data structures and the algorithms and applications associated with them, you'll focus on the trade-offs between speed and storage and the benefits of deploying concurrency when appropriate. This book will demonstrate the huge increases in application performance that are possible. The presentation of classic data structures and techniques of algorithm design (greedy, divide and conquer, branch-and-bound to name a few) provides an essential foundation and toolkit for problem solving. But this book goes further by presenting heuristic algorithms and their implementations for solving computationally intractable combinatoric optimization problems such as the travelling salesperson problem. Simulated annealing and genetic algorithms are among the techniques used.The consistent style of coding used throughout this book exploits Go’s ability to implement abstract, generic and constrained generic data types without the use of classes. Although some familiarity with Go is assumed, this book should advance your ability to use Go to tackle server-side applications, games, machine learning, information retrieval and other application domains where speed and storage efficiency is essential.What You'll LearnExplore classical data structures and algorithms aimed at making your applications run faster or require less storageUse the new generic features of Go to build reusable data structuresUtilize concurrency for maximizing application performanceSee the power of heuristic algorithms for computationally intractable problemsEnhance and improve your Go programming skillsWho This Book Is ForPracticing Go software developers and students who wish to advance their programming and problem-solving skills and experience the excitement and see the benefits of using generic data structures and algorithms that utilize concurrency whenever possible.
Generic Pipelines Using Docker: The DevOps Guide to Building Reusable, Platform Agnostic CI/CD Frameworks
by Brandon Atkinson Dallas EdwardsCreate generic pipelines to reduce your overall DevOps workload and allow your team to deliver faster. This book helps you get up to speed on the pros and cons of generic pipeline methodology, and learn to combine shell scripts and Docker to build generic pipelines.In today’s world of micro-services and agile practices, DevOps teams need to move as fast as feature teams. This can be extremely challenging if you’re creating multiple pipelines per application or tech stack. What if your feature teams could utilize a generic pipeline that could build, test, and deploy any application, regardless of tech stack? What if that pipeline was also cloud and platform agnostic? Too good to be true? Well think again! Generic Pipelines Using Docker explores the principles and implementations that allow you to do just that. You will learn from real-world examples and reusable code. After reading this book you will have the knowledge to build generic pipelines that any team can use. What You'll Learn Explore the pros and cons of generic pipeline methodologyCombine shell scripts and Docker to build a generic pipelineImplement a pipeline across CI/CD platformsBuild a pipeline that lends itself well to both centralized and federated DevOps teamsConstruct a modular pipeline with components that can be added, removed, or replaced as needed Who This Book Is ForProfessionals who use DevOps or are part of a DevOps team, and are seeking ways to streamline their pipelines and drive more deployments while using less code
Geneses of Postmodern Art: Technology As Iconology (Routledge Advances in Art and Visual Studies)
by Paul CrowtherPostmodernism in the visual arts is not just another 'ism.' It emerged in the 1960s as a transformation of artistic creativity inspired by Duchamp's idea that the artwork does not have to be physically made by its creator. Products of mass culture and technology can be used just as well as traditional media. This idea became influential because of a widespread naturalization of technology - where technology becomes something lived in as well as used. Postmodern art embodies this attitude. To explain why, Paul Crowther investigates topics such as eclecticism, the sublime, deconstruction in art and philosophy, and Paolozzi's Wittgenstein-inspired works.
Genetic Algorithm Essentials
by Oliver KramerThis book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions
by Frances BuontempoSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. <P><P> Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
Genetic Algorithms and Machine Learning for Programmers
by Frances BuontempoSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods, including tournament and roulette wheels.Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Genetic Algorithms for Pattern Recognition (CRC Press Revivals)
by Sankar K. Pal Paul P. WangSolving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.
Genetic Algorithms in Elixir: Solve Problems Using Evolution
by Sean MoriarityFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.