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.
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.
Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.
A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster.
Simple step-by-step walkthroughs to solve common ML problems with TensorFlow.
Train a neural network to classify images of clothing, like sneakers and shirts, in this fast-paced overview of a complete TensorFlow program.
Train a generative adversarial network to generate images of handwritten digits, using the Keras Subclassing API.
Where the global TensorFlow community meets, bringing together the vibrant and growing ecosystem. Call for proposals open now!
Announcing Course 1 of deeplearning.ai’s TensorFlow Specialization, which teaches you about TensorFlow and how to use its high-level APIs, including Keras, to build neural networks for computer vision. You’ll also learn about convolutional neural networks to improve them.
This course was developed by Google and Udacity as a practical approach to deep learning for software developers. Learn how to build deep learning applications with TensorFlow.
We are committed to fostering an open and welcoming ML community. Join the TensorFlow community and help grow the ecosystem.
Get paid to work on an open-source TensorFlow project this summer! Open to undergrad and graduate students.
Our YouTube Channel focuses on machine learning and AI with TensorFlow. Explore a number of new shows, including TensorFlow Meets, Ask TensorFlow, and Coding TensorFlow.
For up-to-date news and updates from the community and the TensorFlow team, follow @tensorflow on Twitter.