TensorFlow alternatives and similar libraries
Based on the "Artificial Intelligence" category
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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.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):
$ pip install tensorflow
A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To update TensorFlow to the latest version, add
--upgrade flag to the above
Try your first TensorFlow program
>>> import tensorflow as tf >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() b'Hello, TensorFlow!'
For more examples, see the TensorFlow tutorials.
If you want to contribute to TensorFlow, be sure to review the [contribution guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's [code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
Continuous build status
|Raspberry Pi 0 and 1||Py3|
|Raspberry Pi 2 and 3||Py3|
Community Supported Builds
|Linux AMD ROCm GPU Nightly||Nightly|
|Linux AMD ROCm GPU Stable Release||Release 1.15 / 2.x|
|Linux s390x Nightly||Nightly|
|Linux s390x CPU Stable Release||Release|
|Linux ppc64le CPU Nightly||Nightly|
|Linux ppc64le CPU Stable Release||Release 1.15 / 2.x|
|Linux ppc64le GPU Nightly||Nightly|
|Linux ppc64le GPU Stable Release||Release 1.15 / 2.x|
|Linux CPU with Intel® MKL-DNN Nightly||Nightly|
|Linux CPU with Intel® MKL-DNN Stable Release||Release 1.15 / 2.x|
|Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6||1.13.1 PyPI|
- TensorFlow Tutorials
- TensorFlow Official Models
- TensorFlow Examples
- TensorFlow in Practice from Coursera
- TensorFlow: Data and Deployment from Coursera
- Getting Started with TensorFlow 2 from Coursera
- Intro to TensorFlow for Deep Learning from Udacity
- Introduction to TensorFlow Lite from Udacity
- Machine Learning with TensorFlow on GCP
- TensorFlow Blog
- Learn ML with TensorFlow
- TensorFlow Twitter
- TensorFlow YouTube
- TensorFlow Roadmap
- TensorFlow White Papers
- TensorBoard Visualization Toolkit
[Apache License 2.0](LICENSE)
*Note that all licence references and agreements mentioned in the TensorFlow README section above are relevant to that project's source code only.