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This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for their real-world implementation.
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Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of natural language processing (NLP), speech and computer vision tasks.
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This book bridges the gap between theoretical mathematics and practical applications in AI. Whether you're aiming to develop practical skills for AI projects,... Læs mere
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This book provides a comprehensive overview of the concept of digital twins, emphasising its strategic importance across various... Læs mere
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This book focuses on fuzzy logic, a paradigm-shifting approach that challenges the binary simplicity of traditional logic systems. The concept of fuzzy logics is a powerful tool capable of handling imprecise information and accommodating the vagaries of real-world scenarios.
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The book develops a view of AI rooted in the theories and values of Information Science (the social science of knowledge) focusing on whether AI is transforming knowledge access and creation in a significantly positive, equitable, inclusive and sustainable way.
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This book covers advances that have emerged over the last two decades of AI research. Examining both theoretical and practical aspects, it presents... Læs mere
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Suitable for the general reader, Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia is the ideal book for anyone seeking a clear and informed introduction to AI.
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Suitable for the general reader, Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia is the ideal book for anyone seeking a clear and informed introduction to AI.
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This book provides an overview of the theory and practical applications of swarm intelligence in fog computing, beyond 5G networks, and information security.
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This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and... Læs mere
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This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data.