| Summary: | 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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