Introduction to TensorFlow
TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started.
TensorFlow
Learn the foundation of TensorFlow with tutorials for beginners and experts to help you create your next machine learning project.
For JavaScript
Use TensorFlow.js to create new machine learning models and deploy existing models with JavaScript.
For Mobile & IoT
Run inference with TensorFlow Lite on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi.
For Production
Deploy a production-ready ML pipeline for training and inference using TensorFlow Extended (TFX).
TensorFlow ecosystem
TensorFlow provides a collection of workflows to develop and train models using Python, JavaScript, or Swift, and to easily deploy in the cloud, on-prem, in the browser, or on-device no matter what language you use.
Product Roadmap and RFCs
Explore the priorities, focus areas, and expected functionality in the upcoming releases of TensorFlow. Review upcoming RFCs (request for comments) for technical deep dives and to participate in design decisions. Many of these areas are driven by community use cases, and we welcome further contributions.
New to machine learning?
TensorFlow is easier to use with a basic understanding of machine learning principles and core concepts. Learn and apply fundamental machine learning practices to develop your skills and prepare you to begin your next project with TensorFlow.
Sign up for MIT's official introductory course on deep learning methods with applications to machine translation, image recognition, game playing, and more. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.
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.
The deeplearning.ai TensorFlow Specialization teaches you best practices for using TensorFlow's high-level APIs, including Keras, to build neural networks for computer vision, natural language processing, and time series forecasting.