You can use existing models, convert Python TensorFlow models, use transfer learning to retrain existing models with your own data, and develop models from scratch.

Using Tensorflow, which is an open source artificial intelligence library developed by Google, we have studied and compared the effects of multiple activation functions on classification results. The ...

To help with that, [Edje Electronics] has put together a step-by-step guide to using TensorFlow to retrain Google’s Inception object recognizer.

This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn ...

This repository aims to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the wiki associated with this repository. Each tutorial includes source code and ...

The loss function used is multi-class classification. I also implemented the example in Tensorflow (using tf.data.Dataset), but training is slow, and better examples are available on GitHub (svm_layer ...

There are many different binary classification algorithms. In this article I'll demonstrate how to perform binary classification using a deep neural network with the Keras code library. The best way ...

Developers working with Google's TensorFlow Lite for Microcontrollers open source neural network inference engine now have the option to leverage SensiML's powerful automated data labeling and ...