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Are you ready to transform your understanding of AI and unlock the power of Federated Learning? In a world where data privacy is paramount, this breakthrough approach to training machine learning models across devices—without sharing raw data—is revolutionizing the future of artificial intelligence.
Federated Learning in Practice: Training Across Devices Without Sharing Raw Data is the ultimate guide for anyone looking to master this cutting-edge technique. Whether you're a machine learning engineer, a privacy-conscious developer, or simply someone interested in the future of AI, this book will equip you with the knowledge and practical skills to build secure, scalable, and privacy-preserving machine learning systems.
Inside, you’ll learn:
The fundamentals of federated learning and why it's the key to privacy-first AI
How to implement federated learning systems across devices and environments
Strategies to overcome challenges like data heterogeneity, device dropout, and unreliable networks
Techniques to protect sensitive data using secure aggregation and differential privacy
Real-world case studies from industries like healthcare, finance, and mobile AI
Practical, hands-on examples and code in Python with frameworks like TensorFlow Federated and PySyft
This book isn’t just theory—it’s a step-by-step roadmap that will show you how to take your AI projects from concept to deployment. You’ll walk away with the tools and confidence to create models that learn collaboratively, while keeping user data private and secure.
The future of AI is decentralized, privacy-focused, and more powerful than ever. Don’t get left behind—buy this book now and learn how to be part of the next wave of innovation in machine learning.
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Paperback. Condizione: new. Paperback. Are you ready to transform your understanding of AI and unlock the power of Federated Learning? In a world where data privacy is paramount, this breakthrough approach to training machine learning models across devices-without sharing raw data-is revolutionizing the future of artificial intelligence.Federated Learning in Practice: Training Across Devices Without Sharing Raw Data is the ultimate guide for anyone looking to master this cutting-edge technique. Whether you're a machine learning engineer, a privacy-conscious developer, or simply someone interested in the future of AI, this book will equip you with the knowledge and practical skills to build secure, scalable, and privacy-preserving machine learning systems.Inside, you'll learn: The fundamentals of federated learning and why it's the key to privacy-first AIHow to implement federated learning systems across devices and environmentsStrategies to overcome challenges like data heterogeneity, device dropout, and unreliable networksTechniques to protect sensitive data using secure aggregation and differential privacyReal-world case studies from industries like healthcare, finance, and mobile AIPractical, hands-on examples and code in Python with frameworks like TensorFlow Federated and PySyftThis book isn't just theory-it's a step-by-step roadmap that will show you how to take your AI projects from concept to deployment. You'll walk away with the tools and confidence to create models that learn collaboratively, while keeping user data private and secure.The future of AI is decentralized, privacy-focused, and more powerful than ever. Don't get left behind-buy this book now and learn how to be part of the next wave of innovation in machine learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798297858220
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