Altri libri che potrebbero interessarti

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  The Shocking Truth about Genetic...

  Zoey Fraisers

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  Genetic testing has become one of the most talked-about advancements in modern science, promising insights into health, ancestry, and even future risks for inherited conditions. With the rise of at-home DNA testing kits and their widespread availability, more people than ever are exploring what their genetic code can reveal. But behind the promise of knowledge lies a complex web of ethical concerns, privacy risks, and limitations that many consumers are unaware of. While genetic testing can be a powerful tool for medical advancements and personalized healthcare, it also raises important questions about how this information is used, who has access to it, and what unintended consequences may arise.  
  At its core, genetic testing analyzes DNA to identify mutations, traits, or predispositions that can impact a person’s health and well-being. Initially developed for medical use, these tests have expanded beyond hospitals and research labs, becoming a booming industry catering to consumers who want to know more about their ancestry or potential health risks. Companies market these tests as a way to take control of one’s future, offering data on everything from nutrition and fitness compatibility to risks for serious diseases. However, the accuracy and reliability of these consumer-grade tests vary, and their results can sometimes lead to unnecessary panic or false reassurance. Furthermore, genetic testing is not always as straightforward as it seems—genes interact in complex ways, and not all findings have clear implications for an individual’s health.  
  One of the most pressing concerns surrounding genetic testing is privacy. The information encoded in DNA is highly personal, yet many people provide their genetic data to testing companies without fully understanding how it may be stored, shared, or sold.

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  Agile Manifesto - Unpacking the...

  Jacob Richardson

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  Dive into the essence of Agile methodologies with "Agile Manifesto: Unpacking the Agile Manifesto for Modern Development." This comprehensive guide explores each of the core values and principles declared in the Agile Manifesto, providing a deep and actionable understanding tailored for today's dynamic development environments. 
  In February 2001, seventeen software developers crafted the Agile Manifesto to address the inefficiencies of traditional software development practices. They proposed a revolutionary approach centered around flexibility, collaboration, and customer satisfaction. This book revisits these foundational ideas, breaking down complex concepts into practical applications, from small teams to large enterprises. 
  "Agile Manifesto" offers:In-depth analysis of the four core values and twelve principles of Agile.Practical insights on implementing Agile practices such as Scrum, Kanban, and XP within your teams.Strategies for effectively using Agile tools and technologies that enhance collaboration and productivity.Guidance on navigating challenges in distributed teams and scaling Agile methodologies across large organizations.Perspectives on the future of Agile, including its application beyond software development. 
  Whether you are a software developer, project manager, or business leader seeking to instill flexibility and efficiency in your projects, this book provides the tools and knowledge to effectively implement Agile practices. Through real-world case studies, expert interviews, and a clear, engaging narrative, "Agile Manifesto" is an essential resource for anyone looking to foster a truly Agile environment in the modern workplace. Rediscover the Agile Manifesto and transform your approach to software development and beyond. 
   

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  Neural Networks for Beginners -...

  James Ferry

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  "Neural Networks for Beginners: Unlock the Secrets of Neural Networks" is your essential guide to understanding and mastering one of artificial intelligence's most powerful tools. Whether you're new to the world of neural networks or looking to deepen your understanding, this beginner-friendly book demystifies complex concepts and empowers you to harness the full potential of neural networks. 
  From the fundamentals of neural network architecture to practical applications in image recognition, natural language processing, and beyond, this book covers everything you need to know to get started. You'll learn about neurons, layers, activation functions, and the training process, gaining a solid foundation in the building blocks of neural networks. 
  With step-by-step instructions and real-world examples, you'll discover how to implement neural networks using popular Python libraries like TensorFlow and Keras. Whether you're building your first neural network or exploring advanced techniques, this book provides clear guidance and hands-on exercises to help you succeed. 
  But Neural Networks for Beginners goes beyond the basics. It also explores the challenges and limitations of neural networks, including data quality, interpretability, and ethical considerations. Plus, you'll get a glimpse into the future of neural networks, with insights into emerging trends like explainable AI and quantum neural networks. 
  Neural Networks for Beginners is your roadmap to mastering one of the most transformative technologies of our time. With this book as your guide, you'll unlock the secrets of neural networks and unleash their power to solve complex problems and drive innovation in AI. 
   

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  Aromatherapy Essentials:...

  Rebecca Morgan

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  "Aromatherapy Essentials: Harnessing the Power of Essential Oils: Discover Healing Blends for Mind, Body, and Spirit" is an in-depth exploration of the powerful world of essential oils and their remarkable benefits. This comprehensive guide takes readers through aromatherapy's history, science, and practical applications, revealing how these natural extracts can profoundly enhance physical, emotional, and spiritual well-being. The book provides detailed insights into the properties and uses of a wide range of essential oils, offering practical advice on creating custom blends tailored to specific needs and preferences. 
  With clear instructions and easy-to-follow recipes, "Aromatherapy Essentials" empowers beginners and seasoned practitioners to successfully and properly use essential oils in their daily lives. Whether you seek to alleviate stress, improve concentration, promote better sleep, or deepen your spiritual practice, this book offers a wealth of knowledge and resources to assist you in reaching your wellness objectives. The emphasis on sustainable and ethical practices highlights the importance of responsible sourcing and mindful consumption, ensuring that the benefits of aromatherapy extend to both personal health and environmental well-being. 
  In addition to practical applications, the book delves into the science behind aromatherapy, presenting evidence-based research and expert insights that bridge traditional wisdom with modern understanding. Readers will learn about the physiological and psychological mechanisms essential oils exert their effects, gaining a deeper appreciation for the intricate connections between mind, body, and spirit.

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  Cobol - Cobol Code Optimization

  Andy Vickler

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  This audiobook is a practical, hands-on guide to COBOL code optimization. It provides basic principles and techniques for writing efficient, high-level COBOL programs. The audiobook assumes that you are an experienced programmer and want to learn how to develop high performance applications using the latest tools and techniques. The audiobook is also a practical guide to developing high performance applications by optimizing both COBOL code and the application. 
  The audiobook targets IT professionals who are responsible for the day-to-day development and maintenance of business applications written in COBOL. The audiobook introduces essential concepts of performance analysis and optimization. It also covers many tools and techniques including some advanced techniques such as parallelization, multiprocessing, virtual machines, and XML parsing. With the help of hands-on examples, real world case studies, process maps, and a full-scale application, this audiobook teaches professionals and students alike how to build high performance applications using the latest techniques for optimizing COBOL code using safe hooks provided by COBOL compilers as well as unit testing services. 
  This audiobook will teach programmers how to identify and eliminate redundant code with COBOL code optimization techniques. It includes high-level, simple to understand illustrations on program design, programming principles, data structures, debugging techniques, and provides a complete overview of COBOL programming language. 
  If you truly see yourself as a coding enthusiast, then this is the audiobook for you. Even if you're a newcomer to coding, this audiobook will make you an expert in COBOL code optimization, and help you diminish many of the painful effects of repeated errors from careless errors.

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  Low-Code AI - A Practical...

  Michael Abel, Gwendolyn Stripling

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  Take a data-first and use-case-driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems. 
   
   
   
  Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications. 
   
   
   
  You'll learn how to distinguish between structured and unstructured data and the challenges they present; visualize and analyze data; preprocess data for input into a machine learning model; differentiate between the regression and classification supervised learning models; compare different ML model types and architectures, from no code to low code to custom training; design, implement, and tune ML models; and export data to a GitHub repository for data management and governance.

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