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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 Amazon Bedrock: Build, scale, and secure generative AI applications using Amazon Bedrock
by Bunny Kaushik Shikhar KwatraBecome proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standardsKey FeaturesLearn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist ArchitectsMaster the core techniques to develop and deploy several AI applications at scaleGo beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learnExplore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performanceExplore several architecture patterns for different business use casesGain insights into ethical AI practices, model governance, and risk mitigation strategiesEnhance your skills in employing agents to develop intelligence and orchestrate tasksMonitor and understand metrics and Amazon Bedrock model responseExplore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standardsWho this book is forThis book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.
Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
by Ben Auffarth2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key FeaturesLearn how to leverage LangChain to work around LLMs’ inherent weaknessesDelve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challengesGet better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into realityBook DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learnCreate LLM apps with LangChain, like question-answering systems and chatbotsUnderstand transformer models and attention mechanismsAutomate data analysis and visualization using pandas and PythonGrasp prompt engineering to improve performanceFine-tune LLMs and get to know the tools to unleash their powerDeploy LLMs as a service with LangChain and apply evaluation strategiesPrivately interact with documents using open-source LLMs to prevent data leaksWho this book is forThe book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.
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 Art with JavaScript and SVG: Utilizing Scalable Vector Graphics and Algorithms for Creative Coding and Design (Design Thinking)
by David MatthewThis book introduces you to the exciting world of generative art and creative coding through the medium of JavaScript and Scalable Vector Graphics (SVG). Using tried and trusted techniques, you’ll tackle core topics such as randomness and regularity, noise and naturalistic variance, shape and path creation, filter effects, animation, and interactivity. In the process you’ll learn SvJs, a JavaScript library that closely mirrors the SVG spec and makes scripting SVG intuitive and enjoyable. You’ll also study the craft of generative art and its creative process, along with JavaScript fundamentals, using modern ES6+ syntax. Each chapter will build upon the previous one, and those completely new to programming will be given a primer to help them find their feet. Generative Art with JavaScript and SVG will take you on a fun journey, peppered with plenty of sketches throughout, designed not only to explain, but to inspire. You Will: • Structure and randomise compositions. • Understand the different types of randomness and their probability distributions. • Create organic variance with the SvJs Noise module. • Apply SVG filter effects in a generative fashion. • Explore different approaches to animating with SVG. • Make your compositions dynamic and interactive. WHO IS IT FOR: Web developers and designers and creative coders with an interest in digital and generative art as well as artists who are interested in learning to code with JavaScript.
Generative Artificial Intelligence: Exploring the Power and Potential of Generative AI
by Shivam R Solanki Drupad K KhublaniThis book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models. The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today’s AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation. By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will Learn What is Generative Artificial Intelligence? What are text-to-image synthesis techniques and conditional image generation? What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques? What are text-to-video models and how do you tune them? What are large language models, and how do you tune them? Who This Book Is For Those with intermediate to advanced technical knowledge in artificial intelligence and machine learning
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 Intelligence and Intelligent Tutoring Systems: 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10–13, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14799)
by Angelo Sifaleras Fuhua LinThis book constitutes the refereed proceedings of the 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024, held in Thessaloniki, Greece, during June 10–13, 2024. The 35 full papers and 28 short papers included in this book were carefully reviewed and selected from 88 submissions. This book also contains 2 invited talks. They were organized in topical sections as follows: Generative Intelligence and Tutoring Systems; Generative Intelligence and Healthcare Informatics; Human Interaction, Games and Virtual Reality; Neural Networks and Data Mining; Generative Intelligence and Metaverse; Security, Privacy and Ethics in Generative Intelligence; and Generative Intelligence for Applied Natural Language Processing.
Generative Intelligence and Intelligent Tutoring Systems: 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10–13, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14798)
by Angelo Sifaleras Fuhua LinThis book constitutes the refereed proceedings of the 20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024, held in Thessaloniki, Greece, during June 10–13, 2024. The 35 full papers and 28 short papers included in this book were carefully reviewed and selected from 88 submissions. This book also contains 2 invited talks. They were organized in topical sections as follows: Generative Intelligence and Tutoring Systems; Generative Intelligence and Healthcare Informatics; Human Interaction, Games and Virtual Reality; Neural Networks and Data Mining; Generative Intelligence and Metaverse; Security, Privacy and Ethics in Generative Intelligence; and Generative Intelligence for Applied Natural Language Processing.
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.
The Genesis of Logic: Reflections on the Origins, Principles and Paths of Common-sense Reasoning (Fuzzy Management Methods)
by Enric TrillasThe Genesis of Logic addresses the principles of common-sense reasoning, which are employed in everyday decision-making processes and extend beyond deductive reasoning alone. Linked to language, logic inherits its flexibility. These are a few laws, the 'formal skeleton of reasoning,' based on the relationship of linguistic inference that, while needing to be represented in each context, allow for the consideration of non-comparable, orthogonal statements. By facilitating deduction and abduction, speculation emerges as a fundamental intellectual operation. As a whole, this work offers a new genetic-evolutionary perspective to reconsider Logic, a panoramic outlook that examines laws outside the skeleton as local laws, necessary for the validity of specialized reasoning. It moves away from the rigid reticular structure of sets of statements and views induction as the search for speculations, non-monotonic reasoning as speculative, and conjecture, only proven in finite Boolean algebras,that reasoning involves following paths of inference in a zigzag pattern, alternating between deduction and abduction.
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.
Genetic Algorithms in Java Basics
by Lee Jacobson Burak KanberGenetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own. Genetic algorithms are frequently used to solve highly complex real world problems and with this book you too can harness their problem solving capabilities. Understanding how to utilize and implement genetic algorithms is an essential tool in any respected software developers toolkit. So step into this intriguing topic and learn how you too can improve your software with genetic algorithms, and see real Java code at work which you can develop further for your own projects and research. Guides you through the theory behind genetic algorithms Explains how genetic algorithms can be used for software developers trying to solve a range of problems Provides a step-by-step guide to implementing genetic algorithms in Java What you'll learn How to construct genetic algorithms in Java which you can extend for your own projects and research What genetic algorithms are and the biological inspiration behind them How genetic algorithms can be implemented to solve problems Solving a traveling salesman problem and how to apply a genetic algorithm to it How to use a genetic algorithm to solve timetabling problems How a genetic algorithm can be used to build a robotic controller Applying optimization techniques to genetic algorithms Who this book is for Genetic Algorithms in Java Basics is perfect for developers, researchers and students who are working on problems where genetic algorithms may be a solution and need to program real, working code in Java. It's also suitable for all Java developers who are curious about genetic algorithms who would like a practical, hands-on introduction to genetic algorithms using Java. Table of Contents 1. Introduction 2. Implementation of a Basic Algorithm 3. Robot Controllers 4. Traveling 5. Class Scheduling 6. Optimization
Genetic and Evolutionary Computing
by Jerry Chun-Wei Lin Jeng-Shyang Pan Shu-Chuan Chu Chien-Ming ChenThis volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2014, the 8th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by Nanchang Institute of Technology in China, Kaohsiung University of Applied Science in Taiwan, and VSB-Technical University of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one of is the capital of Jiangxi Province in southeastern China, located in the north-central portion of the province. As it is bounded on the west by the Jiuling Mountains, and on the east by Poyang Lake, it is famous for its scenery, rich history and cultural sites. Because of its central location relative to the Yangtze and Pearl River Delta regions, it is a major railroad hub in Southern China. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing.