TensorFlow is a powerful open source software library for high performance numerical computation using data flow graphs. 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. The framework helps build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
TensorFlow comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains. It has many pre-built functions to ease the task of building different neural networks. TensorFlow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine.
TensorFlow provides a Python API, as well as a less documented C++ API.
TensorFlow is by far the most popular machine learning library. It’s used by Google, OpenAI, NVIDIA, Intel, SAP, eBay, Airbus, Uber, Airbnb, Snap, Dropbox and startups.
- Efficient, flexible, scalable, and maintainable.
- Python API.
- Portability: deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
- Save and restore models, graphs.
- Auto-differentiation autodiff.
- Provides an extensive suite of functions and classes that allow users to define models from scratch.
- Provides users with pre-built algorithms for simpler model development.
- Supplies large data sets for training individual models.
- Many high level APIs are built on top of TensorFlow.
- Flexibility: Available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. Run the software from Raspberry Pi, Android, Windows, iOS, Linux to server farms.
The software was originally developed by researchers and engineers from the Google Brain team within Google’s AI organization.
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