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Mobile intelligence — TensorFlow Lite classification on Android (added support for TF2.0)
Adding the first Machine Learning model into your mobile app
This post was originally published at thinkmobile.dev — a blog about implementing intelligent solutions in mobile apps (link).
*** Edit, 23.04.2019 ***
TensorFlow 2.0 experimental support
In the repository, you can find Jupyter Notebook with the code running on TensorFlow 2.0 alpha, with the support for GPU environment (up to 3 times faster learning process). As this is not yet stable version, the entire code may break in any moment. The notebook was created just for the Colaboratory environment. It requires some changes to make it working on Docker environment described in the blog post.
The notebook is available here.
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This is my new series about using Machine Learning solutions in mobile apps. As the opposition to the majority of articles, there will be not much about building layers, training processes, fine-tuning, playing with Google TPUs and data science in general.
Instead, we’ll focus on understanding how to plug in models into apps, use, debug and optimize them, and be effective in the cooperation with data scientists and AI…