Unlock the potential of AI in image recognition with this beginner-friendly guide. Designed for those new to the field, this book covers essential concepts like how computers interpret images, the types of data involved, and the basic pipeline of computer vision systems. You'll learn practical steps to prepare and organize image data, understand key machine learning principles, and explore foundational neural network architectures. The book also introduces techniques for training robust models, leveraging existing models through transfer learning, and evaluating performance. With clear explanations and accessible guidance, you'll gain the confidence to build your own image recognition systems and understand their real-world applications. - Understand how computers analyze and interpret images - Learn to prepare and label image datasets effectively - Explore fundamental neural network architectures for vision tasks - Discover techniques to improve model accuracy and robustness - Gain insights into deploying AI-powered image recognition systems responsibly Empower your journey into AI with practical skills and foundational knowledge.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Unlock the potential of AI in image recognition with this beginner-friendly guide. Designed for those new to the field, this book covers essential concepts like how computers interpret images, the types of data involved, and the basic pipeline of computer vision systems. You'll learn practical steps to prepare and organize image data, understand key machine learning principles, and explore foundational neural network architectures. The book also introduces techniques for training robust models, leveraging existing models through transfer learning, and evaluating performance. With clear explanations and accessible guidance, you'll gain the confidence to build your own image recognition systems and understand their real-world applications. Understand how computers analyze and interpret images Learn to prepare and label image datasets effectively Explore fundamental neural network architectures for vision tasks Discover techniques to improve model accuracy and robustness Gain insights into deploying AI-powered image recognition systems responsiblyEmpower your journey into AI with practical skills and foundational knowledge. Bestandsnummer des Verkäufers 9798295535468
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Paperback. Zustand: new. Paperback. This book introduces A/B testing in clear, practical terms for adult readers who want to understand how online experiments drive better decisions in business, product design, and everyday problem solving. Using straightforward examples, it explains how small, controlled changes can answer real questions-such as which headline performs better, which layout increases sign-ups, or which feature improves retention. The focus is on building sound judgment, data literacy, and evidence-based decision making.You'll learn how real organizations use experiments to reduce guesswork and measure impact. A famous large-scale web experiment once tested dozens of shades of blue and showed that tiny design changes can significantly affect user behavior-an example that illustrates why careful measurement matters and why intuition alone often fails.Rather than presenting A/B testing as a perfect process, the book emphasizes the moments where learning happens: failures, setbacks, and course corrections. Each chapter highlights what didn't work, what the experiment revealed, and how teams adjusted their approach-showing how experiments turn mistakes into useful information.Chapters walk you through forming testable hypotheses, choosing clear success metrics, setting up fair comparisons, and interpreting results without overclaiming. You'll also learn plain-language explanations of sample size, statistical noise, and common pitfalls.The book includes short exercises and prompts that help you practice designing experiments, tracking outcomes, and communicating findings. It also covers basic ethical safeguards-how to avoid harming user experience, monitor unintended effects, and stop tests that create problems-so experimentation remains responsible as well as effective. 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 9798295535468
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Taschenbuch. Zustand: Neu. AI for Image Recognition for Beginners | Design and Analyze Experiments to Compare Different Versions of a Product or Feature | Zoe Hollow | Taschenbuch | for Beginners | Englisch | 2025 | Little Big Giant | EAN 9798295535468 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 134453507
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