Yolo Object Detection Explained
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Yolo Object Detection Explained
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-12
Yolo Object Detection Explained written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-12 with Computers categories.
"YOLO Object Detection Explained" "YOLO Object Detection Explained" offers a comprehensive and accessible journey through the landscape of modern object detection, illuminating the path from its classical foundations to the cutting-edge innovations that define today’s real-time vision systems. The book artfully traces the evolution of detection techniques, contrasting the architectural shifts from traditional handcrafted methods to sophisticated deep learning models like YOLO, SSD, and R-CNN, while contextualizing these advancements within real-world applications and benchmark-driven progress. Through this historical and technical narrative, readers gain not only a deep understanding of the field but also an appreciation for the performance breakthroughs that have made real-time object perception possible. Central to the book is an in-depth exploration of the YOLO architecture itself—its unified, end-to-end philosophy, grid-based prediction mechanisms, and continuous refinement across successive versions. With clarity and rigor, the text guides practitioners through the entire YOLO lifecycle, from preparing augmented datasets and configuring models, to mastering advanced training strategies and overcoming deployment challenges across diverse hardware and edge environments. Specialized chapters tackle optimization, postprocessing, quantization, robustness, and production-scale serving, equipping the reader with practical insights for building and maintaining high-performance detection pipelines. Beyond the core technology, "YOLO Object Detection Explained" addresses the nuanced realities of customizing YOLO for advanced and ethical applications. The book examines scenario-specific adaptations—ranging from healthcare and agriculture to autonomous vehicles and smart cities—while delving into the vital topics of adversarial security, bias mitigation, privacy, and explainability. It concludes with a forward-looking perspective on the future of object detection, surveying hybrid approaches, continual and federated learning, multimodal sensing, and the evolving benchmarks that will shape next-generation intelligent vision systems. This work stands as an essential resource for engineers, researchers, and decision-makers seeking both mastery of the present and a roadmap to the future of object detection.
Deep Learning For Computer Vision
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2019-04-04
Deep Learning For Computer Vision written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-04 with Computers categories.
Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Machine Learning For Real World Applications
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Author : Dinesh K. Sharma
language : en
Publisher: Springer Nature
Release Date : 2024-09-20
Machine Learning For Real World Applications written by Dinesh K. Sharma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-20 with Mathematics categories.
This book provides a comprehensive coverage of machine learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.
Advances In Intelligent Manufacturing And Robotics
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Author : Andrew Tan
language : en
Publisher: Springer Nature
Release Date : 2024-02-26
Advances In Intelligent Manufacturing And Robotics written by Andrew Tan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Technology & Engineering categories.
This book presents selected peer reviewed articles from the International Conference on Intelligent Manufacturing and Robotics (ICIMR 2023) held on 22-23 August at the Xi’an Jiaotong-Liverpool University in China. The book deliberates on the key challenges, engineering and scientific discoveries, innovations, and advances in intelligent manufacturing and robotics that are non-trivial through the lens of Industry 4.0. In this book, traditional and modern solutions that are employed across the spectrum of various intelligent manufacturing and robotics contexts are discussed. The book provides an insightful view on the current trends, issues, mitigating factors as well as proposed solutions.
Using Traditional Design Methods To Enhance Ai Driven Decision Making
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Author : Nguyen, Tien V. T.
language : en
Publisher: IGI Global
Release Date : 2024-01-10
Using Traditional Design Methods To Enhance Ai Driven Decision Making written by Nguyen, Tien V. T. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-10 with Computers categories.
In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials.
Mind Mapping And Artificial Intelligence
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Author : Jose Maria Guerrero
language : en
Publisher: Academic Press
Release Date : 2022-10-22
Mind Mapping And Artificial Intelligence written by Jose Maria Guerrero and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-22 with Science categories.
In the near future, we will see an increase in the development and use of all sorts of AI applications. Some of the more promising areas will be Finance, Healthcare, IoT, Manufacturing, Journalism, and Cybersecurity. Many of these applications generate a great amount of complex information. Natural Language Understanding is one of the most clear examples. Traditional ways of visualizing complex information, namely linear text, web pages and hyperlink-based applications, have serious productivity problems. Users need a lot of time to visualize the information and have problems seeing the whole picture of the results. Mind mapping is probably the only way of reducing the problems inherent in these traditional ways of visualizing complex information. Most people have no clear idea about the advantages of mind mapping or the problems created by the traditional ways of visualizing complex information. The goal of Mind Mapping and Artificial Intelligence is to provide readers with an introduction to mind mapping and artificial intelligence, to the problems of using traditional ways of visualizing complex information and as an introduction to mind mapping automation and its integration into Artificial Intelligence applications such as NLU and others. As more applications of Artificial Intelligence are developed in the near future, the need for the improvement of the visualization of the information generated will increase exponentially. Information overload will soon also happen in AI applications. This will diminish the advantages of using AI. Author José Maria Guerrero is a long-time expert in mind mapping and visualization techniques. In this book he also introduces readers to MindManager mind mapping software, which can considerably reduce the problems associated with the interpretation of complex information generated by Artificial Intelligence software. - Provides coverage of the fundamentals of mind mapping and visualization applied to Artificial Intelligence applications - Includes coverage of the scientific bases for mind mapping for the visualization of complex information - Introduces MindManager software for mind mapping - Introduces the author's MindManager toolkit for the readers to use in development of new mind mapping applications - Includes case studies and real-world applications of MindManager for AI applications, including examples using IBM Watson NLU
Practical Machine Learning For Computer Vision
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Author : Valliappa Lakshmanan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-07-21
Practical Machine Learning For Computer Vision written by Valliappa Lakshmanan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-21 with Computers categories.
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Hands On Convolutional Neural Networks With Tensorflow
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Author : Iffat Zafar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-28
Hands On Convolutional Neural Networks With Tensorflow written by Iffat Zafar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-28 with Computers categories.
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
Cognitive Computing And Cyber Physical Systems
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Author : Prakash Pareek
language : en
Publisher: Springer Nature
Release Date : 2024-01-04
Cognitive Computing And Cyber Physical Systems written by Prakash Pareek and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-04 with Computers categories.
This 2-volume set constitutes the post-conference proceedings of the 4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023, Bhimavaram, Andhra Pradesh, India, during August 4-6, 2023. The theme of IC4S 2023 was: cognitive approaches with machine learning and advanced communications. The 70 full papers were carefully reviewed and selected from 165 submissions. The papers are clustered in thematical issues as follows: machine learning and its applications; cyber security and signal processing; image processing; smart power systems; smart city eco-system and communications.
Future Data And Security Engineering Big Data Security And Privacy Smart City And Industry 4 0 Applications
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Author : Tran Khanh Dang
language : en
Publisher: Springer Nature
Release Date : 2024-11-26
Future Data And Security Engineering Big Data Security And Privacy Smart City And Industry 4 0 Applications written by Tran Khanh Dang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-26 with Computers categories.
This two-volume set CCIS 2309-2310 constitutes the refereed proceedings of the 11th International Conference on Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, FDSE 2024, held in Binh Duong, Vietnam, during November 27–29, 2024. The 44 full papers, 12 short papers and 1 keynote paper were carefully reviewed and selected from 189 submissions. They were organized in topical sections as follows: advances in machine learning for big data analytics; security and privacy engineering; data analytics and healthcare systems; smart city and industry 4.0 applications; big data query processing and optimization; and short papers; security and data engineering.