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  General history of industry

  Albert Bleunard

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  From ancient forges to modern assembly lines, General History of Industry brings to life humanity’s epic journey of ingenuity and transformation. This audiobook unveils pivotal technological shifts, daring pioneers, and silent revolutions that have shaped the ways we produce, trade, and live. Blending vivid storytelling, economic insights, and surprising anecdotes, it traces the birth of factories, the rise of energy, standardization, globalization, and the digital era. An immersive panorama that reveals how industry reflects, at its core, the ambition, risks, and imagination of societies. Listen to understand the past, illuminate the present, and glimpse the future.

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  Field Guide to Medicinal Wild...

  Bradford Angier

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  A revision of the definitive reference, containing plant characteristics, distribution, and medicinal qualities, an updated taxonomy, and fifteen new species.   The Field Guide to Medicinal Wild Plants is the ultimate handbook for identifying and using wild plants for medicinal purposes. This illustrated guide to North American wild medicinals has been a nature classic for over thirty years. In this second edition, David K. Foster revises Bradford Angier's invaluable reference, updating the taxonomy and adding more than a dozen species, including the purple coneflower, popularly known as echinacea, as well as ephedra, jewelweed, goldenseal, and more. Scientific information for a general audience and full-color illustrations combine with intriguing accounts of the plants’ uses, making this a practical guide for anyone interested in the medicinal uses of wild plants.

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  Data Mesh - A Comprehensive...

  Vincent Berry

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  In "Data Mesh: A Comprehensive Guide to Decentralized Data Architecture," readers are introduced to a transformative approach to data management that challenges conventional centralized systems. This essential guide delves into the principles and practices of Data Mesh, advocating for a paradigm shift towards a domain-oriented, decentralized data architecture. This book is meticulously designed for data architects, CTOs, data engineers, and any professionals involved in data-intensive industries who seek to overcome the limitations of traditional data management and unlock greater flexibility and scalability. 
  The guide starts with a thorough introduction to the core concepts of Data Mesh, including its four foundational pillars: domain-oriented decentralization of data ownership, data as a product, self-serve data infrastructure, and computational governance. Each section not only explains the theoretical aspects but also illustrates practical implementations through case studies and real-world scenarios, providing readers with a clear pathway to adopting Data Mesh in their organizations. 
  Further chapters are dedicated to the technical implementations of Data Mesh, detailing the technological stack and strategies required to support a scalable and efficient decentralized data architecture. The book also addresses common challenges and pitfalls, offering expert advice on navigating cultural shifts, integrating legacy systems, and maintaining data interoperability. 
  "Data Mesh: A Comprehensive Guide to Decentralized Data Architecture" also explores the future prospects of Data Mesh, including its role in leveraging artificial intelligence and machine learning within a decentralized framework. It outlines how Data Mesh can drive innovation, enhance data security, and improve data compliance in a rapidly evolving digital landscape.

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  Python Playground: An...

  Lisa Johnson

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  For young students excited to explore the world of coding, "Python Playground: An Interactive Guide to Python Programming for Kids: Learn Coding Through Fun Projects and Games" is the ideal place to start. Python is one of the world's most widely used and adaptable programming languages, and this book makes it simple and fun for youngsters to learn. It turns the sometimes complex programming world into a playground of creativity and exploration. 
  This book is not just a theoretical guide, but a practical handbook that makes learning to code enjoyable and interactive. It's filled with interesting, practical tasks that engage young learners and make the learning process fun. Each chapter introduces essential programming concepts like variables, loops, conditionals, and functions, accompanied by step-by-step instructions and concise explanations. Through the creation of engaging games, vibrant graphics, and interactive tests, children will have the opportunity to put what they have learned into practice while also developing their problem-solving and coding logic comprehension. 
  The book also introduces Popular Python libraries like `turtle` and `random` to promote experimentation and ignite creativity. By the conclusion of the book, young programmers will have the self-assurance and abilities necessary to create their projects, which they can then show off to friends and family. 
  "Python Playground" is an invitation to explore, create, and have fun with technology rather than merely a guide to coding. This book offers countless opportunities for those interested in learning by doing and are in the process of becoming budding programmers.

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  The Art of Machine Learning -...

  Craig Dames

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  Machine learning is a subset of artificial intelligence that enables computers to learn from data and make decisions without being explicitly programmed. It is a field that has grown significantly over the past few decades, revolutionizing industries such as healthcare, finance, and technology. By leveraging algorithms and statistical models, machine learning systems can identify patterns, make predictions, and continuously improve their performance. 
  The foundation of machine learning lies in data. Without data, machine learning models cannot function effectively. Data can come in various forms, including structured numerical data, unstructured text, images, and audio. The process of training a machine learning model involves feeding it large amounts of data, allowing it to recognize relationships and make informed predictions. 
  Machine learning is broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, where it learns to map inputs to outputs based on example pairs. This approach is commonly used in applications such as email spam detection and medical diagnosis. Unsupervised learning, on the other hand, deals with data that lacks labels. The model identifies patterns and structures within the data without prior guidance, making it useful for tasks like customer segmentation and anomaly detection. Reinforcement learning is a more dynamic approach where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. This technique is widely used in robotics and game development.

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  Entropy - A Very Short...

  James Binney

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  Until the middle of the nineteenth century entropy and energy were confused with one another. The intellectual fog slowly cleared and now these words name subtle and powerful concepts. Most people think they know what energy is, but few could define entropy.This Very Short Introduction traces the emergence of entropy and energy as distinct concepts, and explains how entropy spread from thermodynamics into statistical mechanics, probability theory and data science. It explains the implications of entropy for heat pumps, solar cells, carbon capture, and liquified natural gas. Quantum mechanics and gravity open new horizons for entropy, and when combined in the quantum theory of black holes, they call into question our current understanding of material reality.

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