TFQ consists of structures such as qubits, gates, circuits, and measurement operators required for specifying quantum computations.
Google is celebrating the first anniversary of TensorFlow Quantum (TFQ), a library for rapid prototyping of hybrid quantum-classical ML models. TFQ is regarded as a tipping point for developments in hybrid quantum and classic machine learning models the company has been pushing for years.
An end-to-end open-source platform for Machine Learning. Before we start with TensorFlow, we will need to know what machine learning and deep learning technologies are.
This is a detailed guide on how to create TensorFlow models and then deploy them using TensorFlow Serving
This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task.
Keras vs Tensorflow - Learn the differences between Keras and Tensorflow on basis of Ease to use, Fast development,Functionality,flexibility,Performance etc
Deploy a Deep Learning Model to Production using TensorFlow Serving.