Outros livros que poderiam interessá-lo

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  Blockchain Basics + NFTs for...

  Satosy Nacamoto

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  Do you want to invest, but it seems so complicated? 
  Have you never heard about Blockchain, NFTs and DeFi? 
  If you want to invest then you are in the right place… 
  Decentralized finance began with payment in cryptocurrencies but now covers many more activities such as borrowing, lending, saving, decentralized exchanges, and much more. NFT market brings you the unique opportunity to start making money from things once untradeable - digital music, art, and even your tweets on Twitter. Millionaires are being made because of this new opening in the market. 
  In this collection you will know all the secrets to start making moeny in this new world! If you're interested in learning more and grabbing these opportunities with both hands, then this audiobook is for you. By the end of this guide, you will have all the tools necessary to start earning passive income. 
  Scroll to the top of the page and click the “buy now” button to discover all the secrets!

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  Causal Inference and AI -...

  Sam Green

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  Artificial intelligence has revolutionized industries by identifying patterns and making predictions from vast amounts of data. However, the true power of AI lies not just in recognizing correlations but in understanding causation—the fundamental "why" behind events and outcomes. 
  Causal Intelligence provides a comprehensive exploration of how causal inference principles can transform AI systems from mere pattern detectors into tools capable of reasoning, decision-making, and ethical intervention. Combining the rigor of causal theory with the practicalities of modern AI, this book bridges the gap between two evolving fields to unlock groundbreaking applications in healthcare, finance, public policy, and beyond. 
  Whether you’re a data scientist, AI practitioner, researcher, or curious technologist, this book will empower you to:Grasp the fundamental differences between correlation and causation.Understand key causal inference frameworks, including Pearl's Causal Hierarchy and Rubin's Causal Model.Discover techniques for identifying causal relationships in complex data.Learn how to integrate causal reasoning into machine learning and AI systems.Explore real-world applications and case studies where causality transforms outcomes.Address fairness and ethics in AI using a causal perspective. 
  Written in an accessible yet rigorous style, Causal Intelligence serves as a roadmap for the next generation of AI systems—ones that don’t just predict the future but understand it, shape it, and improve it. Dive into the emerging world of causal AI and discover how it’s poised to redefine data-driven decision-making for the better.

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  The Cholera Years - The United...

  Charles E. Rosenberg

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  Cholera was the classic epidemic disease of the nineteenth century, as the plague had been for the fourteenth. Its defeat was a reflection not only of progress in medical knowledge but of enduring changes in American social thought. Rosenberg has focused his study on New York City, the most highly developed center of this new society. Carefully documented, full of descriptive detail, yet written with an urgent sense of the drama of the epidemic years, this narrative is as absorbing for general audiences as it is for the medical historian. In a new Afterword, Rosenberg discusses changes in historical method and concerns since the original publication of The Cholera Years. 
   
  “A major work of interpretation of medical and social thought . . . this volume is also to be commended for its skillful, absorbing presentation of the background and the effects of this dread disease.”—I.B. Cohen, New York Times 
   
  ”The Cholera Years is a masterful analysis of the moral and social interest attached to epidemic disease, providing generally applicable insights into how the connections between social change, changes in knowledge and changes in technical practice may be conceived.”—Steven Shapin, Times Literary Supplement 
   
  ”In a way that is all too rarely done, Rosenberg has skillfully interwoven medical, social, and intellectual history to show how medicine and society interacted and changed during the 19th century. The history of medicine here takes its rightful place in the tapestry of human history.”—John B. Blake, Science

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  Computer Systems and Networking...

  Saimon Carrie

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  "Computer Systems and Networking for Beginners" is your comprehensive guide to mastering advanced strategies and techniques in computer systems and networking, designed specifically for those seeking optimal efficiency and scalability in their digital endeavors. Whether you're a novice eager to delve into the intricate world of computer systems or an experienced professional looking to enhance your skills, this book provides the knowledge and insights you need to navigate the complexities of modern computing environments with confidence. 
  From understanding the fundamental principles of computer systems architecture to implementing advanced networking protocols and optimizing system performance, this book covers a broad spectrum of topics essential for building robust and scalable computing infrastructures. Explore key concepts such as virtualization, cloud computing, cybersecurity, and network design, and learn how to leverage cutting-edge technologies to streamline operations and maximize productivity. 
  Featuring practical examples, real-world case studies, and hands-on exercises, "Computer Systems and Networking for Beginners" offers a step-by-step approach to mastering complex concepts and techniques. Whether you're interested in deploying scalable network architectures, securing sensitive data, or optimizing system resources for peak performance, this book equips you with the tools and expertise needed to succeed in today's dynamic IT landscape. 
  "Computer Systems and Networking for Beginners" is your indispensable companion on the journey to mastering advanced strategies and techniques in computer systems and networking. Dive into the world of digital innovation, and unlock the full potential of your computer systems and networks with this essential guide. 
   

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  Agile Project Management with...

  Alexa Campbell

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  Gain a broader understanding of Agile project management using Scrum. 
  Do you want to make your team more agile? 
  Do you want to be able to recover quickly when the unexpected happens? 
  Do you want to stay focused on what's important and get rid of tasks that aren't? 
  The answer is Scrum, a project management framework that lets you do just that. It can help you organize your team and get everyone working together towards shared goals, no matter how big or small the project. In Agile Project Management With Scrum, we'll show you how to use this powerful tool to create a more effective and efficient workflow. 
  In this book, you will:Discover and take a deep dive into the science behind Scrum.Understand the basics of agile projects and then dive into the management of agile projects.Discover the ScrumMaster.Learn about planning a Scrum project.Master DSDM and its techniques.Understand agile project developmentLearn about the deployment processDiscover the use of Scrum in scaling projects. 
  This book will teach you how to use the Scrum method of project management, which uses short sprints and constant feedback from your team members to help you stay on track and deliver your project on time. 
  It's full of practical advice and easy-to-follow steps that can be applied to any project, from small to large.

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  Machine learning

  Bright Mills

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  Machine Learning (ML) is a subset of Artificial Intelligence that empowers systems to learn from data and make decisions or predictions without being explicitly programmed. It represents a major shift from traditional rule-based programming to systems that adapt and improve over time. This comprehensive guide delves into the theoretical foundations, types, algorithms, tools, applications, challenges, and the future of Machine Learning. It covers supervised, unsupervised, semi-supervised, and reinforcement learning in detail, explains key techniques such as neural networks, decision trees, support vector machines, and deep learning, and explores real-world applications across domains like healthcare, finance, security, and robotics. Ethical concerns, bias in AI, explainability, and interpretability are also discussed, offering a well-rounded understanding of the state and direction of Machine Learning.

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