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Author: Hrishikesh Andurlekar
Publisher: Orange Education Pvt Ltd
ISBN: 8197953457
Category : Computers
Languages : en
Pages : 502
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Book Description
TAGLINE Elevate your Unreal Engine skills with This Essential Guide to AI-Driven Game Development! KEY FEATURES ● Comprehensive coverage of AI techniques and their Unreal Engine implementation ● Practical hands-on exercises and real-world examples for seamless AI integration ● Covers all important AI workflows in Unreal Engine DESCRIPTION In the rapidly evolving world of game development, integrating Artificial Intelligence (AI) into your Unreal Engine projects is no longer optional—it’s essential. Mastering AI for Unreal Engine is your definitive guide to unlocking the transformative power of AI, propelling your games beyond the ordinary and into the realm of cutting-edge, immersive experiences. Through a step-by-step approach, you will explore a wide range of AI techniques, including pathfinding, behavior trees, perception systems, and the powerful Environment Query System (EQS). Each chapter delves into the theoretical underpinnings of these AI components, followed by hands-on exercises and real-world examples that demonstrate their integration within Unreal Engine. We will learn to create intelligent, dynamic, and engaging game experiences by leveraging the power of AI. What sets this book apart is its deep dive into emerging AI frameworks like the Mass Framework, giving you a strategic edge in adopting the latest trends and technologies that are shaping the future of gaming. You’ll learn how to optimize your AI systems, enhance performance, and master debugging techniques critical for smooth, efficient development processes. Mastering AI for Unreal Engine doesn’t just teach you AI—it empowers you to innovate, create intelligent game worlds, and stand out in the fiercely competitive game development landscape. WHAT WILL YOU LEARN ● Set up and navigate Unreal Engine to establish a strong foundation for AI development. ● Learn Blueprints, create class Blueprints, and use Blueprint communication for interactive AI components. ● Build AI characters, and master navigation meshes and pathfinding for dynamic movement. ● Create complex AI behaviors using Behavior Trees and Blackboard components. ● Implement AI Perception to add sight and hearing, making your AI characters more aware and reactive. ● Use State Trees to manage AI states with evaluators, tasks, and transitions. ● Leverage the Environmental Query System (EQS) to enable AI to make context-aware decisions. ● Debug and optimize AI with Unreal’s visual logging and debugging tools. ● Explore advanced AI design techniques like Data Oriented Design, Mass AI, and ZoneGraph. WHO IS THIS BOOK FOR? This book is tailored for game developers, aspiring designers, and anyone interested in integrating AI into Unreal Engine projects. Suitable for beginners to experienced professionals, it’s ideal for students, hobbyists, and industry pros looking to enhance their AI skills. Whether you're creating Non-Player Characters (NPCs), designing complex decision-making systems, or advancing game AI, this book provides the tools and knowledge you need to succeed. TABLE OF CONTENTS 1. Introduction to Unreal Engine 2. Unreal Blueprints 3. Understanding AI in Games 4. Navigation and Pathfinding 5. Behavior Trees and Blackboards 6. AI Perception 7. StateTree 8. Environment Query System 9. SmartObjects 10. Debugging AI in Unreal Engine 11. Mass Framework and Future Trends in GameAI Index
Author: Hrishikesh Andurlekar
Publisher: Orange Education Pvt Ltd
ISBN: 8197953457
Category : Computers
Languages : en
Pages : 502
Get Book Here
Book Description
TAGLINE Elevate your Unreal Engine skills with This Essential Guide to AI-Driven Game Development! KEY FEATURES ● Comprehensive coverage of AI techniques and their Unreal Engine implementation ● Practical hands-on exercises and real-world examples for seamless AI integration ● Covers all important AI workflows in Unreal Engine DESCRIPTION In the rapidly evolving world of game development, integrating Artificial Intelligence (AI) into your Unreal Engine projects is no longer optional—it’s essential. Mastering AI for Unreal Engine is your definitive guide to unlocking the transformative power of AI, propelling your games beyond the ordinary and into the realm of cutting-edge, immersive experiences. Through a step-by-step approach, you will explore a wide range of AI techniques, including pathfinding, behavior trees, perception systems, and the powerful Environment Query System (EQS). Each chapter delves into the theoretical underpinnings of these AI components, followed by hands-on exercises and real-world examples that demonstrate their integration within Unreal Engine. We will learn to create intelligent, dynamic, and engaging game experiences by leveraging the power of AI. What sets this book apart is its deep dive into emerging AI frameworks like the Mass Framework, giving you a strategic edge in adopting the latest trends and technologies that are shaping the future of gaming. You’ll learn how to optimize your AI systems, enhance performance, and master debugging techniques critical for smooth, efficient development processes. Mastering AI for Unreal Engine doesn’t just teach you AI—it empowers you to innovate, create intelligent game worlds, and stand out in the fiercely competitive game development landscape. WHAT WILL YOU LEARN ● Set up and navigate Unreal Engine to establish a strong foundation for AI development. ● Learn Blueprints, create class Blueprints, and use Blueprint communication for interactive AI components. ● Build AI characters, and master navigation meshes and pathfinding for dynamic movement. ● Create complex AI behaviors using Behavior Trees and Blackboard components. ● Implement AI Perception to add sight and hearing, making your AI characters more aware and reactive. ● Use State Trees to manage AI states with evaluators, tasks, and transitions. ● Leverage the Environmental Query System (EQS) to enable AI to make context-aware decisions. ● Debug and optimize AI with Unreal’s visual logging and debugging tools. ● Explore advanced AI design techniques like Data Oriented Design, Mass AI, and ZoneGraph. WHO IS THIS BOOK FOR? This book is tailored for game developers, aspiring designers, and anyone interested in integrating AI into Unreal Engine projects. Suitable for beginners to experienced professionals, it’s ideal for students, hobbyists, and industry pros looking to enhance their AI skills. Whether you're creating Non-Player Characters (NPCs), designing complex decision-making systems, or advancing game AI, this book provides the tools and knowledge you need to succeed. TABLE OF CONTENTS 1. Introduction to Unreal Engine 2. Unreal Blueprints 3. Understanding AI in Games 4. Navigation and Pathfinding 5. Behavior Trees and Blackboards 6. AI Perception 7. StateTree 8. Environment Query System 9. SmartObjects 10. Debugging AI in Unreal Engine 11. Mass Framework and Future Trends in GameAI Index
Author: Anik Acharjee
Publisher: Orange Education Pvt Ltd
ISBN: 9348107682
Category : Computers
Languages : en
Pages : 490
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Book Description
TAGLINE Transform Your Web App Development Journey with MERN and AI KEY FEATURES ● Utilize AI for code generation, debugging, and optimizing performance in MERN applications. ● Build AI-powered web apps with real-time data processing and user behavior insights. ● Integrate AI capabilities seamlessly with MongoDB, Express.js, React, and Node.js for scalable web solutions. DESCRIPTION With AI applications driving a projected $15.7 trillion boost to the global economy by 2030, combining AI with the popular MERN stack has become a game-changer for developers and businesses alike. Mastering AI App Development with MERN Stack is a hands-on guide designed for developers ready to bring AI capabilities to their MERN applications, covering everything from foundational machine learning to advanced, real-world solutions. Starting with the essentials of setting up a MERN development environment, the book guides readers through machine learning basics in JavaScript, enabling AI integration with Node.js and TensorFlow.js. Each chapter provides practical insights into building intelligent interfaces with React, effective data handling with MongoDB, and AI middleware using Express.js. Readers will learn to create features like AI-powered chatbots, image and voice recognition, and personalized recommendation systems. Real-world scenarios and case studies demonstrate how AI can elevate MERN applications. With guidance on security practices, deployment, and scaling, this book is a complete toolkit for building secure, production-ready AI solutions with MERN. Mastering AI with the MERN Stack empowers developers to unlock the full potential of AI in the MERN ecosystem, creating innovative, impactful applications for an AI-driven world. WHAT WILL YOU LEARN ● Integrate AI into MERN applications for improved user experiences. ● Build AI-powered web apps using the MERN stack effectively. ● Implement real-time data processing and personalized content features. ● Leverage pre-trained AI models for language and analytics tasks. ● Design scalable AI architectures to enhance performance and capacity. WHO IS THIS BOOK FOR? This book is tailored for JavaScript developers, full-stack developers and frontend or backend developers interested in AI integration into their web applications. It’s also ideal for web developers aiming to create dynamic applications and MERN stack enthusiasts exploring AI's potential. With a basic understanding of the MERN stack, readers will find this guide a valuable resource for advancing their web development careers by incorporating AI capabilities. TABLE OF CONTENTS 1. Introduction to AI and the MERN Ecosystem 2. Setting Up the MERN Development Environment 3. Fundamentals of Machine Learning with JavaScript 4. Implementing AI with Node.js and TensorFlow.js 5. Creating Intelligent User Interfaces with React 6. Data Management for AI with MongoDB 7. Building AI Middleware with Express.js 8. Crafting AI-Powered Chatbots 9. Image and Voice Recognition Capabilities 10. Personalization with Recommendation Systems 11. Deploying MERN and AI Applications 12. Security Practices for AI-Enabled MERN Applications 13. Scaling AI Features in Production 14. Emerging Trends in AI and MERN Development 15. Case Studies and Real-World Success Stories Index
Author: Jeremy Kahn
Publisher: Simon and Schuster
ISBN: 1668053349
Category : Computers
Languages : en
Pages : 336
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Book Description
A Fortune magazine journalist draws on his expertise and extensive contacts among the companies and scientists at the forefront of artificial intelligence to offer dramatic predictions of AI’s impact over the next decade, from reshaping our economy and the way we work, learn, and create to unknitting our social fabric, jeopardizing our democracy, and fundamentally altering the way we think. Within the next five years, Jeremy Kahn predicts, AI will disrupt almost every industry and enterprise, with vastly increased efficiency and productivity. It will restructure the workforce, making AI copilots a must for every knowledge worker. It will revamp education, meaning children around the world can have personal, portable tutors. It will revolutionize health care, making individualized, targeted pharmaceuticals more affordable. It will compel us to reimagine how we make art, compose music, and write and publish books. The potential of generative AI to extend our skills, talents, and creativity as humans is undeniably exciting and promising. But while this new technology has a bright future, it also casts a dark and fearful shadow. AI will provoke pervasive, disruptive, potentially devastating knock-on effects. Leveraging his unrivaled access to the leaders, scientists, futurists, and others who are making AI a reality, Kahn will argue that if not carefully designed and vigilantly regulated AI will deepen income inequality, depressing wages while imposing winner-take-all markets across much of the economy. AI risks undermining democracy, as truth is overtaken by misinformation, racial bias, and harmful stereotypes. Continuing a process begun by the internet, AI will rewire our brains, likely inhibiting our ability to think critically, to remember, and even to get along with one another—unless we all take decisive action to prevent this from happening. Much as Michael Lewis’s classic The New New Thing offered a prescient, insightful, and eminently readable account of life inside the dot-com bubble, Mastering AI delivers much-needed guidance for anyone eager to understand the AI boom—and what comes next.
Author: Ivana Bartoletti
Publisher: John Wiley & Sons
ISBN: 1119551900
Category : Business & Economics
Languages : en
Pages : 304
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Book Description
Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 149
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Book Description
In an era shaped by the rapid evolution of artificial intelligence, grappling with the ethical dimensions of AI technologies has become an imperative. "Mastering AI Ethics" is a groundbreaking guide that delves deep into the complexities of AI ethics, equipping readers with the insights required to navigate the ethical challenges posed by AI innovations. About the Book: In this thought-provoking book, readers are invited to explore the intricate web of ethical considerations surrounding AI development, deployment, and societal impact. With real-world examples, case studies, and actionable frameworks, "Mastering AI Ethics" empowers readers to make informed decisions and contribute to a future where AI serves the greater good. Key Features: Foundations of Ethical AI: The book lays a strong foundation by demystifying the core concepts that underpin AI ethics. Readers will develop a clear understanding of how ethical considerations intersect with AI technologies and why these intersections are crucial. Tackling Complex Ethical Dilemmas: Through a series of real-world scenarios, readers will grapple with intricate ethical dilemmas presented by AI. The book guides readers in analyzing and evaluating these scenarios, enabling them to cultivate the critical thinking skills needed to confront ethical challenges head-on. Frameworks for Ethical Decision-Making: "Mastering AI Ethics" introduces readers to practical frameworks and models designed to facilitate ethical decision-making in AI contexts. These frameworks empower readers to weigh conflicting interests, anticipate potential harms, and arrive at ethically sound solutions. Promoting Transparency and Accountability: The book delves into the concepts of transparency and accountability in AI development and deployment. Readers will discover how to foster transparency, hold AI systems accountable, and ensure responsible use of AI technologies. Societal Implications of AI: By examining broader societal implications, the book explores how AI influences areas such as privacy, bias, fairness, and social justice. Readers will gain insights into how AI technologies can amplify existing inequalities and how to design AI systems that mitigate these effects. Collaborative Ethical Practices: "Mastering AI Ethics" underscores the significance of collaborative efforts in shaping AI ethics. Readers will learn how interdisciplinary collaboration involving ethicists, technologists, policymakers, and stakeholders can drive more ethical AI development and deployment. Exploring Future Ethical Challenges: As AI continues to evolve, so do its ethical considerations. The book provides a forward-looking perspective on emerging trends in AI ethics, from the ethical implications of AI in healthcare to the challenges posed by autonomous systems and AI-driven decision-making. Who Should Read This Book: "Mastering AI Ethics" is an indispensable resource for AI practitioners, data scientists, ethicists, policymakers, and anyone concerned with the ethical implications of AI technologies. Whether you're an AI researcher aiming to integrate ethics into your work, a business leader exploring responsible AI implementation, or a curious citizen intrigued by AI's impact on society, this book equips you with the tools to engage in meaningful discussions and drive ethical change in the AI landscape.
Author: Andrew Hinton
Publisher: Book Bound Studios
ISBN: 1761590073
Category : Business & Economics
Languages : en
Pages : 309
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Book Description
Dive into the "AI Mastery Trilogy," the ultimate collection for professionals seeking to conquer the world of artificial intelligence (AI). This 3-in-1 compendium is meticulously crafted to guide you from the foundational principles of AI to the intricate mathematical frameworks and practical coding applications that will catapult your expertise to new heights. Book 1: "AI Basics for Managers" by Andrew Hinton is your gateway to understanding and implementing AI in business. It equips managers with the knowledge to navigate the AI landscape, identify opportunities, and lead their organizations toward a future of innovation and growth. Book 2: "Essential Math for AI" demystifies the mathematical backbone of AI, offering a deep dive into the core concepts that fuel AI systems. From linear algebra to game theory, this book is a treasure trove for anyone eager to grasp the numerical and logical foundations that underpin AI's transformative power. Book 3: "AI and ML for Coders" is the hands-on manual for coders ready to harness AI and machine learning in their projects. It provides a comprehensive overview of AI and ML technologies, practical coding advice, and ethical considerations, ensuring you're well-equipped to create cutting-edge, responsible AI applications. The "AI Mastery Trilogy" is more than just a set of books; it's a comprehensive learning journey designed to empower business leaders, mathematicians, and coders alike. Whether you're looking to lead, understand, or build the future of AI, this collection is an indispensable resource for mastering the art and science of one of the most exciting fields in technology. Embrace the AI revolution and secure your copy of the "AI Mastery Trilogy" today!
Author: Chiheb Chebbi
Publisher: Packt Publishing Ltd
ISBN: 178899311X
Category : Language Arts & Disciplines
Languages : en
Pages : 264
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Book Description
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Author: Andreas M. Antonopoulos
Publisher: O'Reilly Media
ISBN: 1491971916
Category : Computers
Languages : en
Pages : 424
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Book Description
Ethereum represents the gateway to a worldwide, decentralized computing paradigm. This platform enables you to run decentralized applications (DApps) and smart contracts that have no central points of failure or control, integrate with a payment network, and operate on an open blockchain. With this practical guide, Andreas M. Antonopoulos and Gavin Wood provide everything you need to know about building smart contracts and DApps on Ethereum and other virtual-machine blockchains. Discover why IBM, Microsoft, NASDAQ, and hundreds of other organizations are experimenting with Ethereum. This essential guide shows you how to develop the skills necessary to be an innovator in this growing and exciting new industry. Run an Ethereum client, create and transmit basic transactions, and program smart contracts Learn the essentials of public key cryptography, hashes, and digital signatures Understand how "wallets" hold digital keys that control funds and smart contracts Interact with Ethereum clients programmatically using JavaScript libraries and Remote Procedure Call interfaces Learn security best practices, design patterns, and anti-patterns with real-world examples Create tokens that represent assets, shares, votes, or access control rights Build decentralized applications using multiple peer-to-peer (P2P) components
Author: Enes Bilgin
Publisher: Packt Publishing Ltd
ISBN: 1838648496
Category : Computers
Languages : en
Pages : 544
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Book Description
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook Description Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems. What you will learnModel and solve complex sequential decision-making problems using RLDevelop a solid understanding of how state-of-the-art RL methods workUse Python and TensorFlow to code RL algorithms from scratchParallelize and scale up your RL implementations using Ray's RLlib packageGet in-depth knowledge of a wide variety of RL topicsUnderstand the trade-offs between different RL approachesDiscover and address the challenges of implementing RL in the real worldWho this book is for This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
