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This book offers detailed insight into various business models that can be easily implemented using cutting-edge... Læs mere
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This volume focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records.
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Industry 4.0 has greatly enhanced the intricacy and interconnectedness of industrial operations. Industrial Internet of Things... Læs mere
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The book focuses on concepts to give readers a wider perspective on generative AI systems such as ChatGPT. It examines algorithmic elements pertaining to the improvement of current tools. It also examines how generative AI can lead to social benefits.
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Science LTD examines the evolving relationship between scientific knowledge, technology, and society in an era where deep tech companies are increasingly driven by discoveries rather than market demand.
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Science LTD examines the evolving relationship between scientific knowledge, technology, and society in an era where deep tech companies are increasingly driven by discoveries rather than market demand.
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Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts.
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The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. This book eases you... Læs mere
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Test-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical pipelines and processes.
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This book offers a comprehensive discussion of the growing importance of ethical considerations in algorithmic systems to introduce practical frameworks and tools for identifying ethical risks, ensuring fair outcomes, and maintaining transparency in algorithmic processes.
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This book offers a comprehensive discussion of the growing importance of ethical considerations in algorithmic systems to introduce practical frameworks and tools for identifying ethical risks, ensuring fair outcomes, and maintaining transparency in algorithmic processes.