This practical guide helps school leaders leverage the power of AI to explore possible solutions to problems and generate actionable steps toward positive change. Imagine a world where educators can boost their productivity, task management and overall well-being with the aid of an AI assistant. In this groundbreaking book, an experienced school leader offers practical strategies for leveraging AI to support a more efficient and effective way to work.The 62 strategies in this book will help leaders – including those with limited technical knowledge – use AI tools to address critical aspects of leadership in education, such as collaborative decision-making, building relationships and trust, personalized professional learning, data analysis and improvement, and parent and community engagement.Featured examples show how using AI can speed up or eliminate administrative tasks, leaving more time for human interaction. With detailed prompts and instructions on how to write them, the book offers fun, innovative ideas that promote work-life balance and sustainable wellness in leadership roles, with strategies for managing workload and fostering personal growth.The book:Follows a structured format, with each example offering a problem, solution, action steps, acceleration tips and cautions. Shows how to formulate effective AI prompts that yield accurate and meaningful responses from AI tools. Addresses safety and ethical considerations, highlighting potential risks, challenges and cautions school leaders need to be aware of when using AI-powered solutions. Whether you’re an experienced leader or just starting out, this book equips you with the tools and insights needed to lead with confidence, collaboration and compassion. Stay ahead of the curve and embrace the transformative potential of AI with this essential resource.Audience: Education leaders and administrators Read more

A New York Times bestsellerSuperintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life. The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time. Read more

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. Read more

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. Read more

Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (AI). The 4th Edition brings readers up to date on the latest technologies,presents concepts in a more unified manner, and offers new or expanded coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI. Read more

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Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aurélien Géron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Vincent Pollet, Head of Research at Nuance: "The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."Everything you really need to know in Machine Learning in a hundred pages. Read more

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Mastering AI Prompt Engineering: The Ultimate Guide for ChatGPT UsersWhat if you could unlock the full potential of AI with just a few carefully chosen words? Imagine transforming a tool into a creative collaborator, a problem solver, and a source of limitless innovation—simply by mastering the art of prompting.Why This Book?The quality of AI-generated content is directly tied to the prompts you create. With "Mastering AI Prompt Engineering," you will learn how to:Maximize AI Effectiveness: Generate accurate, engaging content by crafting precise prompts.Enhance Creativity: Discover strategic AI techniques that inspire innovative ideas and solutions.Solve Problems: Guide AI to think critically and solve complex challenges with ease.What You'll Learn:Communicate Effectively with AI: Understand AI capabilities and limitations to make your prompts more effective.Advanced Prompting Techniques: Master Chain of Thought (CoT) prompting, Reasoning and Correction (ReAct), and other strategies to refine AI responses.Practical Applications: Apply your skills across professional, educational, and creative domains.Key Highlights:Core Principles: Learn the fundamentals of AI communication with proven frameworks, such as Aristotle's four causes.Building Blocks of Prompts: Explore how language elements shape high-quality prompts.Advanced Techniques and Real-World Examples: Practice and adapt through exercises and scenarios tailored to real-world applications.New Chapter: Chained PromptingDiscover how to break down complex tasks into a series of interconnected prompts, demonstrating how each step builds upon the previous one to create refined, cohesive outputs.Take Action Now!Order your copy today and start mastering the art of AI prompting. Elevate your productivity, creativity, and success in every AI-driven task! Read more

Learn to research, plan, design, and test the UX of AI-powered productsUnlock the future of design with UX for AI―your indispensable guide to not only surviving but thriving in a world powered by artificial intelligence. Whether you're a seasoned UX designer or a budding design student, this book offers a lifeline for navigating the new normal, ensuring you stay relevant, valuable, and indispensable to your organization. In UX for AI: A Framework for Designing AI-Driven Products, Greg Nudelman―a seasoned UX designer and AI strategist―delivers a battle-tested framework that helps you keep your edge, thrive in your design job, and seize the opportunities AI brings to the table. Drawing on insights from 35 real-world AI projects and acknowledging the hard truth that 85% of AI initiatives fail, this book equips you with the practical skills you need to reverse those odds. You'll gain powerful tools to research, plan, design, and test user experiences that seamlessly integrate human-AI interactions. From practical design techniques to proven user research methods, this is the essential guide for anyone determined to create AI products that not only succeed but set new standards of value and impact. Inside the book:Hands-on exercises: Build your confidence and skills with practice UX design tasks like Digital Twin and Value Matrix, which you can immediately apply to your own AI projects.Common AI patterns and best practices: Explore design strategies for LLMs (Large Language Models), search engines, copilots, and more.Proven user research strategies: Learn how to uncover user needs and behaviors in this brave new world of AI-powered design.Real-world case studies: See how simple, practical UX approaches have prevented multimillion-dollar failures and unlocked unprecedented value.Perfect for any UX designer working with AI-enabled and AI-driven products, UX for AI is also a must-read resource for designers-in-training and design students with an interest in artificial intelligence and contemporary design. Read more

Here’s the solution you've been waiting for! The Ultimate Guide that simplifies AI concepts into easy-to-understand language while providing practical, real-world examples and ethical considerations.Dive into the world of artificial intelligence without feeling overwhelmed by the technical jargon.Learn to advance your career, shift into a new field, or satisfy your curiosity with practical and ethical AI applications.Most beginners, career shifters, creative minds, and tech enthusiasts face the same challenges. But that can change with the transformative insights you’ll find in this book.In "AI Made Simple for Beginners, "you’ll find everything you need to grasp and master AI, no matter your background.Here’s a sample of what you’ll uncover inside:Real-world applications of AI in industries you might not have consideredHow you can start implementing AI in your career or personal projectsEthical considerations and how to navigate the moral dilemmas of AISimple, interactive, hands-on exercises to apply AI concepts in real-timeCase studies showing how AI has transformed businesses and industriesThe latest trends in AI and how to stay updated without overwhelmHow to utilize AI for creative applications like art, music, and writingThe role of data in AI and how to understand and manage it effectivelyCommon misconceptions about AI debunkedHow to transition into an AI-related career and the steps to get startedAdvice from AI experts sharing their invaluable insightsPractical AI project ideas: you can start with minimal resourcesHow to balance your AI projects with ethical considerationsOnline resources and communities for continuous learning and support...and much more!You might be thinking, "I don't have a technical background; can I really understand AI?" Absolutely! This book is tailored specifically for beginners with varied educational backgrounds, ensuring you grasp the concepts quickly and confidently.Unlike other guides, this book doesn’t drown you in technicalities. It empowers you with practical, actionable knowledge that you can apply immediately.If you’re ready to step into the future and master AI, then scroll up and click the "Add to Cart" button now! Read more

The AI arms race isn't coming—it's already here. Is your organization going to survive?AI is the most transformative technology in history, yet it's creating massive anxiety and frustration for CEOs, business leaders, and consultants everywhere.Does this sound like you? Everyone using artificial intelligence in your organization is self-taught. There's no common understanding of how to execute. No best practices or frameworks. No way to share or scale AI expertise. You know your employees need upskilling, but you don't know where to start.Your team needs to start using AI effectively and efficiently if you're going to remain relevant in the next 12-24 months. Meanwhile, competitors that truly master enterprise AI adoption aren't just surviving—they're thriving by creating new value instead of simply reducing costs.In this step-by-step guide to AI transformation, John Munsell reveals how to:✓ Transform your organization into an AI-first powerhouse - even if most of your team is AI-resistant.✓ Implement powerful, proven AI adoption frameworks that align everyone from the C-suite to frontline workers.✓ Create scalable AI prompts that deliver exceptional results every time - without requiring technical expertise.✓ Build a culture where knowledge sharing accelerates adoption across all departments.What separates AI winners from the rest?Drawing from thousands of hours working with hundreds of businesses, John Munsell reveals the missing link that transforms scattered AI efforts into organization-wide AI acceleration: proven frameworks and processes that anyone can understand, implement, and scale.Currently, 86% of workers feel inadequately trained in AI use, yet 79% of leaders know they need AI to stay competitive.This book offers a step-by-step blueprint to help leaders, managers, and professionals integrate AI seamlessly across all departments, creating a workforce that embraces AI instead of fearing it.Why This Book?For Business Leaders: Develop an AI transformation roadmap that aligns with long-term growth.For Managers & Teams: Learn how to implement AI solutions without disrupting workflows.For Educators & Professionals: Gain practical knowledge to future-proof your career in an AI-driven world.Inside INGRAIN AI, you'll discover:The AI Transformation Roadmap - The fast-track way to establish an AI-first culture by focusing on the four pillars of AI Adoption: Shared Language, Empowering Governance, Scalable Skills, and Collaborative LearningThe AI Strategy Canvas™ - A revolutionary framework that transforms how your entire organization thinks about and implements artificial intelligenceScalable Prompt Engineering™ - A practical methodology that turns prompting into a scalable process for consistently getting excellence out of AIThis isn't just another book about why AI matters. This is an actual blueprint for transforming your organization into one where artificial intelligence isn't just a tool but a fundamental part of how everyone thinks and works.Whether you're a CEO plotting your company's future, a department head striving for efficiency, or a professional trying to gain AI expertise, INGRAIN AI provides the proven tools and frameworks to compress time, protect jobs, and build an organization that thrives in the age of AI.FREE BONUS RESOURCESWhen you purchase this book, you'll get free access to valuable tools and resources, including a complete prompt database containing all prompts taught in this book - allowing you to implement these strategies immediately in your business.The gap between AI leaders and followers is widening daily. Don't get left behind. Read more

Revealing the unfolding story of Artificial Intelligence, Richard Susskind presents a short non-technical guide that challenges us to think differently about AI. Susskind brings AI out of computing laboratories, big tech companies, and start-ups - and into everyday life.In recent years, and certainly since the launch of ChatGPT, there has been massive public and professional interest in Artificial Intelligence. But people are confused about what AI is, what it can and cannot do, what is yet to come, and whether AI is good or bad for humanity and civilisation - whether it will provide solutions to mankind's major challenges or become our gravest existential threat. There is also confusion about how we should regulate AI and where we should draw moral boundaries on its use. In How To Think About AI, Richard Susskind draws on his experience of working on AI since the early 1980s. For Susskind, balancing the benefits and threats of artificial intelligence is the defining challenge of our age. He explores the history of AI and possible scenarios for its future. His views on AI are not always conventional. He positions ChatGPT and generative AI as no more than the latest chapter in the ongoing story of AI and claims we are still at the foothills of developments. He argues that to think responsibly about the impact of AI requires us to look well beyond todayâs technologies, suggesting that not-yet-invented technologies will have far greater impact on us in the 2030s than the tools we have today. This leads Susskind to discuss the possibility of conscious machines, magnificent new AI-enabled virtual worlds, and the impact of AI on the evolution of biological humans. Read more

Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systemsKey FeaturesUnderstand the foundations and advanced techniques of building intelligent, autonomous AI agentsLearn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systemsExplore crucial aspects of trust, safety, and ethics in AI agent development and applicationsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents.Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.What you will learnMaster the core principles of GenAI and agentic systemsUnderstand how AI agents operate, reason, and adapt in dynamic environmentsEnable AI agents to analyze their own actions and improviseImplement systems where AI agents can leverage external tools and plan complex tasksApply methods to enhance transparency, accountability, and reliability in AIExplore real-world implementations of AI agents across industriesWho this book is forThis book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems.Table of ContentsFundamentals of Generative AIPrinciples of Agentic SystemsEssential Components of Intelligent AgentsReflection and Introspection in AgentsEnabling Tool Use and Planning in AgentsExploring the Coordinator, Worker, and Delegator ApproachEffective Agentic System Design TechniquesBuilding Trust in Generative AI SystemsManaging Safety and Ethical ConsiderationsCommon Use Cases and ApplicationsConclusion and Future Outlook Read more

Unlock the core math and understand the technical nuances of quantum computing in this detailed guide. Delve into the practicality of NISQ algorithms, and survey promising advancements in quantum machine learning.Key FeaturesDiscover how quantum computing works and delve into the math behind it with practical examplesLearn about and assess the most up-to-date quantum computing topics including quantum machine learningExplore the inner workings of existing quantum computing technologies to understand how they may perform significantly better than their classical counterpartsBook DescriptionDancing with Qubits, Second Edition, is a comprehensive quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. A full description of classical computing and the mathematical underpinnings of quantum computing follows, helping you better understand concepts such as superposition, entanglement, and interference. Next up are circuits and algorithms, both basic and sophisticated, as well as a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments may affect you.This new edition is updated throughout with more than 100 new exercises and includes new chapters on NISQ algorithms and quantum machine learning.Understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is explained thoroughly and with helpful examples, leaving you with a solid foundation of knowledge in quantum computing that will help you pursue and leverage quantum-led technologies.What you will learnExplore the mathematical foundations of quantum computingDiscover the complex, mind-bending concepts that underpin quantum systemsUnderstand the key ideas behind classical and quantum computingRefresh and extend your grasp of essential mathematics, computing, and quantum theoryExamine a detailed overview of qubits and quantum circuitsDive into quantum algorithms such as Grover’s search, Deutsch-Jozsa, Simon’s, and Shor’sExplore the main applications of quantum computing in the fields of scientific computing, AI, and elsewhereWho this book is forDancing with Qubits, Second Edition, is a quantum computing textbook for all those who want to understand and explore the inner workings of quantum computing. This entails building up from basic to some sophisticated mathematics and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, or computer science.Table of ContentsWhy Quantum ComputingThey're Not Old, They're ClassicsMore Numbers than You Can ImaginePlanes and Circles and Spheres, Oh MyDimensionsWhat Do You Mean “Probably”?One QubitTwo Qubits, ThreeWiring Up the CircuitsFrom Circuits to AlgorithmsGetting PhysicalConsidering NISQ AlgorithmsIntroduction to Quantum Machine LearningQuestions about the Future Read more

Building with LLM Agents: A Comprehensive GuideDiscover the cutting-edge world of Large Language Model (LLM) agents with this essential guide for researchers, practitioners, and AI enthusiasts. This book provides a complete framework for transforming static language models into intelligent, autonomous agents capable of reasoning, planning, and solving real-world problems.Unlike other resources, this book bridges the gap between theoretical understanding and practical implementation, offering hands-on guidance and real-world examples. You’ll learn how to design, build, and deploy LLM agents using popular frameworks like LangChain, AutoGen, and DSPy, while exploring advanced topics such as retrieval-augmented generation (RAG), knowledge integration, and ethical considerations.Key Features:Understand the foundational concepts and architecture of LLM agents.Learn core capabilities like reasoning, memory, and planning.Master frameworks and tools for agent development, including production-ready techniques.Address challenges like deployment, monitoring, and safety.Gain insights into future trends and advancements in AI agent technology.With a focus on actionable knowledge and practical application, this book is a must-read for anyone looking to harness the full potential of LLM agents in AI-driven systems. Whether you’re a researcher, developer, or advanced student, this guide will empower you to build intelligent, scalable, and impactful AI solutions.Prerequisites: Familiarity with machine learning, NLP, and Python programming. Read more

Build scalable, secure, and intelligent systems by utilizing IoT architectures, AWS, Azure, AI, and real-world solutions to become a skilled IIoT architectKey FeaturesLeverage IoT, AI/ML, and cloud technologies to unlock industrial potential and drive business innovationWork with labs on real-world edge computing scenarios, integrating AWS, Azure, and open source toolsUse diagnostic and predictive analytics to develop digital twins, improve industrial processes, and manage assetsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn today's automation-driven era, precision is crucial, and the Industrial Internet of Things (IIoT) has made a remarkable impact. This updated second edition explores the technologies fueling the IIoT revolution and shares essential knowledge to enable you to establish remote-access networks.Written by IIoT and AI experts, as well as renowned authors, this book helps you enhance your skills in emerging technologies by introducing new techniques from Azure and AWS and keeping you up to date with the latest advancements. You'll find out how Artificial Intelligence of Things (AIoT) and MLOps apply to IIoT and learn how to handle complex projects confidently. The book covers identifying and connecting industrial data sources from various sensors, advancing from foundational concepts to professional skills. You'll discover how to connect these sensors to cloud networks such as AWS IoT, Azure IoT, and open source IoT platforms, and extract data from the cloud to your devices. Through hands-on experience with tools such as Node-RED, OPC UA, MQTT, NoSQL, defense in depth, and Python, you'll develop streaming and batch-based AI algorithms.By the end of this book, you'll have achieved a professional level of expertise in the cloud, IoT, and AI, and be able to build more robust, efficient, and reliable IoT infrastructure for your industry.What you will learnGet a solid understanding of industrial processes, devices, and protocolsHarness IoT technology to effectively manage industrial use casesDesign and implement an IIoT network flow to continuously monitor the performance of your critical assetsGet to grips with popular cloud-based platforms such as AWS and AzureExplore Edge devices and learn about Edge and fog computing to gather field dataApply diagnostic analytics to real-world data to answer critical workforce questionsDevelop AIoT technology for predictive maintenanceWho this book is forIf you are an IoT architect, developer, AI engineer, or stakeholder involved in designing the architecture systems of the Industrial Internet of Things, this book is for you. The only prerequisite needed is a solid understanding of the Python programming language and networking concepts.Table of ContentsIntroduction to Industrial IOTUnderstanding the Industrial Process and DevicesIndustrial Data Flow and DevicesImplementing the Industrial IoT Data FlowApplying CybersecurityPerforming an Exercise Based on Industrial Protocols and StandardsDeveloping Industrial IoT and ArchitectureImplementing a Custom Industrial IoT PlatformBuilding an AWS Industrial IOT SolutionImplementing an Industrial IOT Data Flow with AWSPerforming a Practical Industrial IoT Solution with AzureImplementing an Industrial IoT Data Flow with AzurePerforming Diagnostic, Maintenance, and Predictive AnalyticsImplementing a Digital Twin – Advanced AnalyticsDeploying an Analytics Model Read more

”This book takes an impossibly broad area of computer science and communicates what working developers need to understand in a clear and thorough way.” - David Jacobs, Product Advance Local Key Features Master the core algorithms of deep learning and AI Build an intuitive understanding of AI problems and solutions Written in simple language, with lots of illustrations and hands-on examples Creative coding exercises, including building a maze puzzle game and exploring drone optimizationAbout The Book “Artificial intelligence” requires teaching a computer how to approach different types of problems in a systematic way. The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies. Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You’ll explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills. What You Will Learn Use cases for different AI algorithms Intelligent search for decision making Biologically inspired algorithms Machine learning and neural networks Reinforcement learning to build a better robot This Book Is Written For For software developers with high school–level math skills. About the Author Rishal Hurbans is a technologist, startup and AI group founder, and international speaker. Table of Contents 1 Intuition of artificial intelligence 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning with Q-learning Read more