TensorFlow alternatives and similar libraries
Based on the "Artificial Intelligence" category.
Alternatively, view tensorflow alternatives based on common mentions on social networks and blogs.
10.0 10.0 L3 TensorFlow VS PyTorchTensors and Dynamic neural networks in Python with strong GPU acceleration
9.7 0.0 L1 TensorFlow VS CNTKMicrosoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
9.6 9.8 L1 TensorFlow VS Eclipse Deeplearning4JModel import deployment framework for retraining models (pytorch, tensorflow,keras) deploying in JVM Micro service environments, mobile devices, iot, and Apache Spark
9.0 0.0 L2 TensorFlow VS tiny-cnnheader only, dependency-free deep learning framework in C++14
4.6 1.2 L3 TensorFlow VS Tulip IndicatorsTechnical Analysis Indicator Function Library in C
3.1 0.0 L1 TensorFlow VS Native System AutomationNative cross-platform system automation
1.7 0.0 TensorFlow VS Tulip CellTulipCell is an Excel add-in providing 100+ technical analysis indicators.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of TensorFlow or a related project?
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
You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.
|Raspberry Pi 0 and 1||Py3|
|Raspberry Pi 2 and 3||Py3|
|Libtensorflow MacOS CPU||Status Temporarily Unavailable||Nightly Binary Official GCS|
|Libtensorflow Linux CPU||Status Temporarily Unavailable||Nightly Binary Official GCS|
|Libtensorflow Linux GPU||Status Temporarily Unavailable||Nightly Binary Official GCS|
|Libtensorflow Windows CPU||Status Temporarily Unavailable||Nightly Binary Official GCS|
|Libtensorflow Windows GPU||Status Temporarily Unavailable||Nightly Binary Official GCS|
- TensorFlow Tutorials
- TensorFlow Official Models
- TensorFlow Examples
- DeepLearning.AI TensorFlow Developer Professional Certificate
- TensorFlow: Data and Deployment from Coursera
- Getting Started with TensorFlow 2 from Coursera
- TensorFlow: Advanced Techniques from Coursera
- Intro to TensorFlow for A.I, M.L, and D.L from Coursera
- Intro to TensorFlow for Deep Learning from Udacity
- Introduction to TensorFlow Lite from Udacity
- Machine Learning with TensorFlow on GCP
- TensorFlow Codelabs
- TensorFlow Blog
- Learn ML with TensorFlow
- TensorFlow Twitter
- TensorFlow YouTube
- TensorFlow model optimization 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.