Next generation computer vision techniques (24 risultati)

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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.Key Features:Explore and create intelligent systems using cutting-edge deep learning techniquesImplement deep learning algorithms and work with revolutionary libraries in PythonGet real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and moreBook Description:With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside.What You Will Learn:Get a practical deep dive into deep learning algorithmsExplore deep learning further with Theano, Caffe, Keras, and TensorFlowLearn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann MachinesDive into Deep Belief Nets and Deep Neural NetworksDiscover more deep learning algorithms with Dropout and Convolutional Neural NetworksGet to know device strategies so you can use deep learning algorithms and libraries in the real worldWho this book is for:This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.…

Taschenbuch. Condizione: Neu. Python Deep Learning | Next generation techniques to revolutionize computer vision, AI, speech and data analysis | Gianmario Spacagna (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2017 | Packt Publishing | EAN 9781786464453 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. 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.…

Taschenbuch. Condizione: Neu. Next-Generation Computer Vision Techniques for Video Analysis | Muhammad Moazzam Jawaid (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798260009666 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more.…

Hardcover. Condizione: new. Hardcover. Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more. 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. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more.…