Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Journal of Big Data

Table 4 List of the most common frameworks and libraries

Framework	License	Core language	Year of release	Homepages
TensorFlow	Apache 2.0	C++ & Python	2015	https://www.tensorflow.org/
Keras	MIT	Python	2015	https://keras.io/
Caffe	BSD	C++	2015	http://caffe.berkeleyvision.org/
MatConvNet	Oxford	MATLAB	2014	http://www.vlfeat.org/matconvnet/
MXNet	Apache 2.0	C++	2015	https://github.com/dmlc/mxnet
CNTK	MIT	C++	2016	https://github.com/Microsoft/CNTK
Theano	BSD	Python	2008	http://deeplearning.net/software/theano/
Torch	BSD	C & Lua	2002	http://torch.ch/
DL4j	Apache 2.0	Java	2014	https://deeplearning4j.org/
Gluon	AWS Microsoft	C++	2017	https://github.com/gluon-api/gluon-api/
OpenDeep	MIT	Python	2017	http://www.opendeep.org/