After living and breathing TensorFlow for about a week, I’m almost ready to write my own TensorFlow code — although I’ll probably adapt someone else’s model, rather than start from scratch.

The Model Maker supports models available on the TensorFlow hub such as the EfficientNet-Lite models. In addition, it supports image classification and text classification.

Google today announced TensorFlow Lite Model Maker, a tool that adapts state-of-the-art machine learning models to custom data sets using a technique known as transfer learning.It wraps machine ...

With TensorFlow Lite, the same models can target mobile phones, IoT devices, and edge computing environments. This makes it possible to train the model once and deploy it to an Android phone, ...

To make TensorFlow easier to use, Google announced eager execution, Eagar execution is an intuitive programming model for Python developers designed to remove the distinction between computational ...

TensorFlow Lite is a slimmed-down version of Google’s TensorFlow framework for training machine learning models. It’s a set of tools used by developers to run TensorFlow models on mobile ...

Models created by TensorFlow can be deployed on most any device to serve predictions. TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback.

Let the OSS Enterprise newsletter guide your open source journey! Sign up here. Google today announced TensorFlow Similarity, a Python package designed to train similarity models with the company ...

TensorFlow helps developers create models for image recognition, natural language processing (NLP), and even robotics, and offers pre-built components such as TensorFlow Lite for mobile apps and ...

TensorFlow Lite (TFLite) was announced in 2017 and Google is now calling it “LiteRT” to reflect how it supports third-party models. TensorFlow Lite for mobile on-device AI has “grown beyond ...