In other words, TensorFlow is a cross-platform framework, which means it works on a wide range of hardware, including GPUs and CPUs and mobile and embedded platforms. Models created using TensorFlow can be deployed on various platforms, including servers, the cloud, mobile, edge devices, browsers, and more. Today, it offers an array of workflows with intuitive, high-level APIs that allow both novices and professionals to develop machine learning models in various languages. However, it has maintained its popularity and established itself as a leader in the AI industry. It has faced competition from alternative platforms like PyTorch and Keras, much like any other platform. Professionals use it all around the world to design text, audio, and picture recognition algorithms. TensorFlow is one of the top-rated tools for developing machine learning and deep learning applications. Initially, it was created for machine learning and deep neural networks research by the Google Brain Team inside Google's Machine Intelligence research group. TensorFlow is the leading open-source AI project for deep learning. Build a Data Pipeline with Azure Synapse and Spark Pool View Project
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