TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. In this article, the author explains how to use Tensorflow.NET to build a neural network.

TensorFlow Lite for Microcontrollers dramatically simplifies the development of these devices, by providing a lean framework to deploy machine learning models on resource-constrained processors.

If your neural network was written using Google’s TensorFlow framework then you’ve had the option of using TensorFlow Mobile, but it doesn’t use any of the phone’s accelerated hardware ...

TensorFlow Lite also supports hardware acceleration with the Android Neural Networks API. TensorFlow Lite models are small enough to run on mobile devices, and can serve the offline use case.

Step-by-step coding a full deep neural network with zero libraries — just logic and Python. #NeuralNetwork #PythonCode #DeepLearning ...

TensorFlow Lite is available on Android and iOS via a C++ API and a Java wrapper for Android developers. On devices that support it, the library can also take advantage of the Android Neural ...

In addition, TensorFlow Lite supports the Android Neural Networks API, Java APIs and C++ APIs. According to Google, developers should look at TensorFlow lite as an evolution of the TensorFlow ...

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph structured data using TensorFlow, its machine learning framework.

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.