What is TensorFlow
TensorFlow is an open-source end-to-end platform for Machine Learning. It provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state of the art of Machine Learning and build scalable ML powered applications.
TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud.
Whether you’re an expert or a beginner, TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Easy model building
Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.
TensorFlow offers multiple levels of abstraction so you can choose the right one for your needs. Build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.
If you need more flexibility, eager execution allows for immediate iteration and intuitive debugging. For large ML training tasks, use the Distribution Strategy API for distributed training on different hardware configurations without changing the model definition.
Robust ML production anywhere
Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.
TensorFlow has always provided a direct path to production. Whether it’s on servers, edge devices, or the web, TensorFlow lets you train and deploy your model easily, no matter what language or platform you use.
Powerful experimentation for research
A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.
Build and train state-of-the-art models without sacrificing speed or performance. TensorFlow gives you the flexibility and control with features like the Keras Functional API and Model Subclassing API for creation of complex topologies. For easy prototyping and fast debugging, use eager execution.
TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.
If you want to learn more about Tensorflow check out this Introduction to Tensorflow.