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Computer Vision (AI and ML Reference handbooks) - Softcover
Buch 12 von 22: AI and ML Reference handbooks
Inhaltsangabe
Master Computer Vision from First Principles to Production Systems
Whether you're a student, AI engineer, software developer, researcher, or interview candidate, this comprehensive guide takes you from the fundamentals of computer vision to building production-ready AI applications. The book is designed as both a practical reference and a graduate-level learning resource, covering the mathematics, algorithms, deep learning architectures, deployment strategies, and real-world implementation of modern computer vision systems.
Inside this book, you'll learn:
- Image fundamentals, pixels, color spaces, and digital image representation
- Classical computer vision techniques including filtering, edge detection, feature extraction, and segmentation
- Convolutional Neural Networks (CNNs) from fundamentals to implementation
- Modern object detection algorithms including YOLO
- Semantic and instance segmentation using U-Net and Mask R-CNN
- Transfer Learning and pretrained vision models
- Vision Transformers (ViTs)
- GANs, Diffusion Models, and Generative Computer Vision
- 3D Computer Vision, Camera Geometry, and Depth Estimation
- Video Analytics, Object Tracking, and Action Recognition
- Foundation Vision Models including CLIP, SAM, Grounding DINO, Florence-2, and DINOv2
- Vision-Language Models and multimodal AI
- Production deployment, optimization, monitoring, and MLOps for Computer Vision
- Three complete end-to-end capstone projects with production workflows
- Interview questions and answers in every chapter
- Practical Python implementations using OpenCV, PyTorch, Ultralytics YOLO, and Hugging Face libraries
Unlike books that focus only on theory or only on code, this guide combines intuitive explanations, mathematical foundations, architecture diagrams, production engineering practices, and hands-on implementations into one complete reference.
Whether you're preparing for technical interviews, building production AI systems, pursuing graduate studies, or expanding your professional skills, this book provides a practical roadmap from pixels to intelligent vision systems.
Perfect for:
- AI & Machine Learning Engineers
- Computer Vision Engineers
- Data Scientists
- Software Developers
- Graduate & Undergraduate Students
- Researchers
- Technical Interview Preparation
- Professionals building production AI applications
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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Computer Vision (AI and ML Reference handbooks)
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Computer Vision (Paperback)
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Paperback. Zustand: new. Paperback. Master Computer Vision from First Principles to Production SystemsWhether you're a student, AI engineer, software developer, researcher, or interview candidate, this comprehensive guide takes you from the fundamentals of computer vision to building production-ready AI applications. The book is designed as both a practical reference and a graduate-level learning resource, covering the mathematics, algorithms, deep learning architectures, deployment strategies, and real-world implementation of modern computer vision systems.Inside this book, you'll learn: Image fundamentals, pixels, color spaces, and digital image representationClassical computer vision techniques including filtering, edge detection, feature extraction, and segmentationConvolutional Neural Networks (CNNs) from fundamentals to implementationModern object detection algorithms including YOLOSemantic and instance segmentation using U-Net and Mask R-CNNTransfer Learning and pretrained vision modelsVision Transformers (ViTs)GANs, Diffusion Models, and Generative Computer Vision3D Computer Vision, Camera Geometry, and Depth EstimationVideo Analytics, Object Tracking, and Action RecognitionFoundation Vision Models including CLIP, SAM, Grounding DINO, Florence-2, and DINOv2Vision-Language Models and multimodal AIProduction deployment, optimization, monitoring, and MLOps for Computer VisionThree complete end-to-end capstone projects with production workflowsInterview questions and answers in every chapterPractical Python implementations using OpenCV, PyTorch, Ultralytics YOLO, and Hugging Face librariesUnlike books that focus only on theory or only on code, this guide combines intuitive explanations, mathematical foundations, architecture diagrams, production engineering practices, and hands-on implementations into one complete reference.Whether you're preparing for technical interviews, building production AI systems, pursuing graduate studies, or expanding your professional skills, this book provides a practical roadmap from pixels to intelligent vision systems.Perfect for: AI & Machine Learning EngineersComputer Vision EngineersData ScientistsSoftware DevelopersGraduate & Undergraduate StudentsResearchersTechnical Interview PreparationProfessionals building production AI applications This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798189866494
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Computer Vision
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Taschenbuch. Zustand: Neu. Neuware - Master Computer Vision from First Principles to Production SystemsWhether you're a student, AI engineer, software developer, researcher, or interview candidate, this comprehensive guide takes you from the fundamentals of computer vision to building production-ready AI applications. The book is designed as both a practical reference and a graduate-level learning resource, covering the mathematics, algorithms, deep learning architectures, deployment strategies, and real-world implementation of modern computer vision systems.Inside this book, you'll learn: - Image fundamentals, pixels, color spaces, and digital image representation- Classical computer vision techniques including filtering, edge detection, feature extraction, and segmentation- Convolutional Neural Networks (CNNs) from fundamentals to implementation- Modern object detection algorithms including YOLO- Semantic and instance segmentation using U-Net and Mask R-CNN- Transfer Learning and pretrained vision models- Vision Transformers (ViTs)- GANs, Diffusion Models, and Generative Computer Vision- 3D Computer Vision, Camera Geometry, and Depth Estimation- Video Analytics, Object Tracking, and Action Recognition- Foundation Vision Models including CLIP, SAM, Grounding DINO, Florence-2, and DINOv2- Vision-Language Models and multimodal AI- Production deployment, optimization, monitoring, and MLOps for Computer Vision- Three complete end-to-end capstone projects with production workflows- Interview questions and answers in every chapter- Practical Python implementations using OpenCV, PyTorch, Ultralytics YOLO, and Hugging Face librariesUnlike books that focus only on theory or only on code, this guide combines intuitive explanations, mathematical foundations, architecture diagrams, production engineering practices, and hands-on implementations into one complete reference.Whether you're preparing for technical interviews, building production AI systems, pursuing graduate studies, or expanding your professional skills, this book provides a practical roadmap from pixels to intelligent vision systems.Perfect for: - AI & Machine Learning Engineers- Computer Vision Engineers- Data Scientists- Software Developers- Graduate & Undergraduate Students- Researchers- Technical Interview Preparation- Professionals building production AI applications. Bestandsnummer des Verkäufers 9798189866494
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