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Building mobile applications with TensorFlow /

Deep learning is an incredibly powerful technology for understanding messy data from the real world-and the TensorFlow machine learning library is the ideal way to harness that power. In this practical report, author Pete Warden, tech lead on the Mobile/Embedded TensorFlow team, demonstrates how to...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Warden, Pete (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, [2017]
Edición:First edition.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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520 |a Deep learning is an incredibly powerful technology for understanding messy data from the real world-and the TensorFlow machine learning library is the ideal way to harness that power. In this practical report, author Pete Warden, tech lead on the Mobile/Embedded TensorFlow team, demonstrates how to successfully integrate a Tensorflow deep-learning model into your Android and iOS mobile applications. Aimed specifically at developers who already have a TensorFlow model successfully working in a desktop environment, this report shows you through hands-on examples how to deploy mobile AI applications that are small, fast, and easy to build. You'll explore use cases for on-device deep learning-such as speech, image, and object recognition-and learn how to deliver interactive applications that complement cloud services. With this report, you'll explore: Use cases including speech, image, and object recognition, translation, and text classification Common patterns for integrating a deep-learning model into your application Several examples for running TensorFlow on Android, iOS, and Raspberry Pi Techniques for testing your deep-learning model inside your application Methods to help you prepare your solution for mobile deployment Optimizing your model for latency, RAM usage, model file size, and binary size. 
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