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 https://caffe2.ai/ or https://pytorch.org/
Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks.
More info https://mxnet.apache.org/
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.
An open source machine learning framework that accelerates the path from research prototyping to production deployment.
More info https://pytorch.org/
tf.keras) is TensorFlow's high-level API for building and training deep learning models