Artificial Intelligence Paradigms Application Practice by Lian (20 results)

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Hardcover. Condition: new. Hardcover. This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for real-world implementation.Over the past decade, deep neural networks have made significant progress. However, effectively applying these networks to solve various practical problems remains challenging, which has limited the widespread application of artificial intelligence. Artificial Intelligence Paradigms for Application Practice is the first to comprehensively address implementation paradigms for deep neural networks in practice. The authors begin by reviewing the development of artificial neural networks and provide a systematic introduction to the tasks, principles, and architectures of deep neural networks. They identify the practical limitations of deep neural networks and propose guidelines and strategies for successful implementation. The book then examines 14 representative applications in urban planning, industrial production, and transportation. For each case, the authors present a landing paradigm that effectively addresses practical challenges supported by illustrations, background information, related work, methods, experiments, and conclusions. The experimental results validate the effectiveness of the proposed implementation approaches.The book will benefit researchers, engineers, undergraduate, and graduate students interested in artificial intelligence, deep neural networks, large models, stable diffusion models, video surveillance, smart cities, intelligent manufacturing, intelligent transportation, and other related areas. This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for their real-world implementation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condition: new. Hardcover. This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for real-world implementation.Over the past decade, deep neural networks have made significant progress. However, effectively applying these networks to solve various practical problems remains challenging, which has limited the widespread application of artificial intelligence. Artificial Intelligence Paradigms for Application Practice is the first to comprehensively address implementation paradigms for deep neural networks in practice. The authors begin by reviewing the development of artificial neural networks and provide a systematic introduction to the tasks, principles, and architectures of deep neural networks. They identify the practical limitations of deep neural networks and propose guidelines and strategies for successful implementation. The book then examines 14 representative applications in urban planning, industrial production, and transportation. For each case, the authors present a landing paradigm that effectively addresses practical challenges supported by illustrations, background information, related work, methods, experiments, and conclusions. The experimental results validate the effectiveness of the proposed implementation approaches.The book will benefit researchers, engineers, undergraduate, and graduate students interested in artificial intelligence, deep neural networks, large models, stable diffusion models, video surveillance, smart cities, intelligent manufacturing, intelligent transportation, and other related areas. This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for their real-world implementation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.

Hardcover. Condition: new. Hardcover. This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for real-world implementation.Over the past decade, deep neural networks have made significant progress. However, effectively applying these networks to solve various practical problems remains challenging, which has limited the widespread application of artificial intelligence. Artificial Intelligence Paradigms for Application Practice is the first to comprehensively address implementation paradigms for deep neural networks in practice. The authors begin by reviewing the development of artificial neural networks and provide a systematic introduction to the tasks, principles, and architectures of deep neural networks. They identify the practical limitations of deep neural networks and propose guidelines and strategies for successful implementation. The book then examines 14 representative applications in urban planning, industrial production, and transportation. For each case, the authors present a landing paradigm that effectively addresses practical challenges supported by illustrations, background information, related work, methods, experiments, and conclusions. The experimental results validate the effectiveness of the proposed implementation approaches.The book will benefit researchers, engineers, undergraduate, and graduate students interested in artificial intelligence, deep neural networks, large models, stable diffusion models, video surveillance, smart cities, intelligent manufacturing, intelligent transportation, and other related areas. This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for their real-world implementation. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for their real-world implementation.