Building Mobile Applications with TensorFlow


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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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