Posted on: Thu, 12/05/2019 - 15:31 By: valentina.janev

CAFFE is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface. More info or

Appache MXNet

Posted on: Thu, 12/05/2019 - 09:51 By: valentina.janev

Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks.


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Posted on: Thu, 12/05/2019 - 09:50 By: valentina.janev

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.


Posted on: Thu, 12/05/2019 - 09:49 By: valentina.janev

An open source machine learning framework that accelerates the path from research prototyping to production deployment.

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Posted on: Thu, 06/06/2019 - 12:37 By: valentina.janev

An end-to-end open source machine learning platform.

  • Flow Models & datasets
    Explore repositories and other resources to find available models, modules and datasets created by the TensorFlow community.
  • Tools
    Explore tools to support and accelerate TensorFlow workflows

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