Machine Learning libraries and frameworks are constantly evolving. However, there is no harmonization with one tool that fits all solutions. It seems quite apparent that as more and more libraries evolve the plethora of Machine Learning libraries to choose from will grow to such levels that they will eventually be shunned and refactored towards the cloud in order to utilize greater data processing requirements for scale out. However, certain libraries have a massive following already in industry as examples of some are listed below. Languages like Python, Java, Scala, and C++ are most suited to such contextual work. However, languages like Go are not far behind either. Most of these libraries are directly related to the progress in academic research in the area which can equally provide an indication of what new approaches can be utilized now and what may be possible in the future.
TensorFlow
DL4J
DataFlow
Flink
Spark
Theano
ScikitLearn
GraphLab
Mahout
SpringXD
