ONNX.js in the Browser (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"ONNX.js in the Browser" "ONNX.js in the Browser" is a comprehensive guide for developers and data scientists eager to bring the power of machine learning directly into web browsers. Starting from the fundamentals, this book delves into the ONNX ecosystem, the motivations and use cases for client-side AI, and the architectural principles of ONNX.js. It explains how browser-based inference can be achieved seamlessly, positioning ONNX.js within the broader landscape of web AI frameworks such as TensorFlow.js and WebDNN. The book walks readers through every stage of browser ML development: from p...
"ONNX.js in the Browser"
"ONNX.js in the Browser" is a comprehensive guide for developers and data scientists eager to bring the power of machine learning directly into web browsers. Starting from the fundamentals, this book delves into the ONNX ecosystem, the motivations and use cases for client-side AI, and the architectural principles of ONNX.js. It explains how browser-based inference can be achieved seamlessly, positioning ONNX.js within the broader landscape of web AI frameworks such as TensorFlow.js and WebDNN.
The book walks readers through every stage of browser ML development: from preparing a robust development environment-detailing JavaScript, WebAssembly, and WebGL requirements-to advanced model conversion, optimization, and debugging techniques. It offers deep technical insights into ONNX.js internals, including backend architecture, kernel implementation, tensor memory management, and error handling. Dedicated chapters focus on maximizing model performance with quantization, browser-specific enhancements, backend selection, and resource-efficient model lifecycle management.
Practicality is at the heart of this book, with real-world case studies showcasing in-browser AI applications for computer vision, NLP, predictive analytics, and more. Readers are guided through secure, private, and compliant client-side deployments, integration with modern web development stacks, progressive web apps, and DevOps pipelines for model delivery. "ONNX.js in the Browser" concludes with a forward-looking perspective on WebGPU, federated learning, and large-scale enterprise adoption-empowering web professionals to create interactive, performant, and privacy-conscious AI solutions at the very edge.
"ONNX.js in the Browser" is a comprehensive guide for developers and data scientists eager to bring the power of machine learning directly into web browsers. Starting from the fundamentals, this book delves into the ONNX ecosystem, the motivations and use cases for client-side AI, and the architectural principles of ONNX.js. It explains how browser-based inference can be achieved seamlessly, positioning ONNX.js within the broader landscape of web AI frameworks such as TensorFlow.js and WebDNN.
The book walks readers through every stage of browser ML development: from preparing a robust development environment-detailing JavaScript, WebAssembly, and WebGL requirements-to advanced model conversion, optimization, and debugging techniques. It offers deep technical insights into ONNX.js internals, including backend architecture, kernel implementation, tensor memory management, and error handling. Dedicated chapters focus on maximizing model performance with quantization, browser-specific enhancements, backend selection, and resource-efficient model lifecycle management.
Practicality is at the heart of this book, with real-world case studies showcasing in-browser AI applications for computer vision, NLP, predictive analytics, and more. Readers are guided through secure, private, and compliant client-side deployments, integration with modern web development stacks, progressive web apps, and DevOps pipelines for model delivery. "ONNX.js in the Browser" concludes with a forward-looking perspective on WebGPU, federated learning, and large-scale enterprise adoption-empowering web professionals to create interactive, performant, and privacy-conscious AI solutions at the very edge.
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