TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. Explore examples of how TensorFlow is used to advance research and build AI-powered applications.
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
TensorFlow is primarily designed for Python but it also provides APIs for other languages like C++, Java and JavaScript making it accessible to developers with different programming backgrounds.
Install TensorFlow with Python's pip package manager. TensorFlow 2 packages require a pip version >19.0 (or >20.3 for macOS). Official packages available for Ubuntu, Windows, and macOS. The TensorFlow Docker images are already configured to run TensorFlow.
Installing TensorFlow with pip is a quick way to start building machine learning models. This guide covers setup, troubleshooting, and advanced configurations for Windows, macOS, and Linux.
Installing and using TensorFlow in Python can be a rewarding experience. By following the steps outlined in this blog, you should be able to install TensorFlow, perform basic tensor operations, build and train models, and follow best practices for managing your projects.
In the Python ecosystem, TensorFlow provides a comprehensive end-to-end platform for building machine learning models – from defining model architecture to deploying in production. It has become one of the most popular deep learning frameworks alongside PyTorch, widely used in industry and academia.
Before starting TensorFlow, a strong foundation in key concepts will help you understand and use the framework effectively. Here are the essential prerequisites for our tutorials:
TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. Explore examples of how TensorFlow is used to advance research and build AI-powered applications.