Mobile Neural Network Framework in Practice (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Mobile Neural Network Framework in Practice" "Mobile Neural Network Framework in Practice" offers an in-depth and authoritative exploration of the rapidly evolving field of mobile deep learning, delivering a comprehensive roadmap from foundational concepts to advanced deployment and optimization. Tracing the historical evolution of neural networks for mobile devices, the book methodically introduces the architectural nuances of mobile processors, the diverse landscape of neural network frameworks, and the myriad application domains-ranging from vision and speech to augmented reality and healt...
"Mobile Neural Network Framework in Practice" "Mobile Neural Network Framework in Practice" offers an in-depth and authoritative exploration of the rapidly evolving field of mobile deep learning, delivering a comprehensive roadmap from foundational concepts to advanced deployment and optimization. Tracing the historical evolution of neural networks for mobile devices, the book methodically introduces the architectural nuances of mobile processors, the diverse landscape of neural network frameworks, and the myriad application domains-ranging from vision and speech to augmented reality and healthcare. Concrete comparisons of cloud, edge, and on-device inference illuminate both the computational challenges and practical solutions for scalable, secure, and privacy-preserving mobile AI. The text provides an expert-level examination of the architectural design patterns that empower neural networks to run efficiently on mobile and embedded hardware. Detailed analyses cover compact and efficient model architectures such as MobileNet and SqueezeNet, sophisticated techniques for model pruning, quantization, and knowledge distillation, as well as operator fusion and graph optimization for runtime acceleration. Comprehensive tutorials on training, converting, and securely deploying models across multiple platforms-including TensorFlow Lite, PyTorch Mobile, Core ML, and ONNX-empower practitioners to tackle the critical issues of compatibility, performance, and reproducibility across devices. Beyond foundational frameworks and optimizations, the book ventures into emerging paradigms and real-world case studies, including federated learning, continual on-device personalization, multi-modal model fusion, and secure deployment strategies. It concludes with rigorous methodologies for testing, profiling, and automated integration, as well as forward-looking insights into next-generation mobile AI hardware and the regulatory, ethical, and research challenges on the horizon. Whether you are a research scientist, industry practitioner, or technology leader, "Mobile Neural Network Framework in Practice" is an essential resource for mastering the state-of-the-art in mobile-centric artificial intelligence.
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