AI Vision Mastery Teaching Machines to Interpret Images: Explore computer vision for image recognition and autonomous systems - Tapa blanda

Sinopsis

Teach machines to see—and make smarter products because of it.

In AI Vision Mastery, you’ll learn how to build end-to-end computer vision systems that recognize objects, understand scenes, and power autonomous decision-making. From the first dataset you label to the model you deploy at the edge, this guide gives you clear, practical steps to turn images and video into reliable insights.

Inside, you’ll discover how to:

• Design robust datasets: collection strategies, annotation quality, and augmentation that actually helps.

• Build modern models: CNNs, Vision Transformers, and state-of-the-art detectors for real projects.

• Solve core tasks: classification, object detection, instance/semantic segmentation, OCR, pose estimation, tracking.

• Ship to production: optimization (quantization, pruning), real-time inference, A/B testing, monitoring, and MLOps.

• Run on any platform: cloud GPUs to tiny edge devices and mobile—without sacrificing accuracy.

• Handle real-world messiness: domain shift, class imbalance, low light, motion blur, and adversarial noise.

• Apply to high-impact uses: quality inspection, medical pre-screening support, retail analytics, traffic systems, robotics, and drones.

With step-by-step workflows, actionable checklists, and code patterns (OpenCV, PyTorch/TensorFlow), this book bridges theory and practice so you can deliver vision systems that are fast, accurate, and production-ready.

• Engineers and data scientists building vision features into products

• Robotics and autonomy teams needing reliable perception

• Students and practitioners seeking real-world, deployment-first guidance

• Product leaders who want repeatable vision pipelines and measurable ROI

If your product can see better, it can decide better—this book shows you how to make that leap.

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