Machine Learning with TensorFlow for Mobile and Ubiquitous Interaction

被引:0
|
作者
Huy Viet Le [1 ]
Mayer, Sven [1 ]
Henze, Niels [1 ]
机构
[1] Univ Stuttgart, Stuttgart, Germany
关键词
Machine learning; classification; supervised learning; tensorflow; ubiquitous computing; mobile device;
D O I
10.1145/3152832.3156559
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Due to the increasing amount of sensors integrated into the environment and worn by the user, a sheer amount of context-sensitive data become available. While interpreting them with traditional methods (e.g., formulas and simple heuristics) is challenging, the latest machine learning techniques require only a set of labeled data. TensorFlow is an open-source library for machine learning which implements a wide range of neural network models. With TensorFlow Mobile, researchers and developers can further deploy the trained models on low-end mobile devices for ubiquitous scenarios. This facilitates the model export and offers techniques to optimize the model for a mobile deployment. In this tutorial, we teach attendees two basic steps to a deployment of neural networks on smartphones: Firstly, we will teach how to develop neural network architectures and train them in TensorFlow. Secondly, we show the process to run the trained models on a mobile phone using TensorFlow Mobile.
引用
收藏
页码:567 / 572
页数:6
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