Comparison of Major Machine Learning Related Libraries

TensorFlow (tensorflow.org):

• Python, auto-differentiation, advanced features (RNN, Conv)
• C/C++ APIs:            Yes (also JS, Swift)
• CUDA:                  Yes
• Distributed computing: Yes
• Production ready:      Yes

PyTorch - Similar to Torch (pytorch.org):

• Python, auto-differentiation, advanced features (RNN, Conv)
• C/C++ APIs:            Yes
• CUDA:                  Yes
• Distributed computing: Yes
• Production ready:      Yes

Chainer - Similar to Torch (chainer.org):

• Python, auto-differentiation, advanced features (RNN, Conv)
• C/C++ APIs:            Yes (Unstable)
• CUDA:                  Yes
• Distributed computing: Yes
• Production ready:      Yes

CNTK:

• A competitive ML lib from Microsoft.

Keras:

• High-level APIs only, interface for TensorFlow, CNTK, etc.

Theano (deeplearning.net/software/theano):

• No longer in development, ceased since 2017

Spark (spark.apache.org):

• Java-based, not competitive to C/C++-based.

OpenCV:

• Computer Vision only.

Shogun:

• High-level APIs only.

Scikit-Learn:

• No auto-differentiation!

Pandas:

• No ML built-in, column data processing

NumPy:

• No ML built-in, N-dimension array processing

SciPy:

• Math functions

Matplotlib:

• Data plotting

More to consider:

• Dlib, Accord.NET, mlpack, DyNet, OpenNN, ML.NET, Sonnet, MXNet, Gluon, DL4J, Onnx, ml.js, brain.js, ConvNet.js, WebDNN, XGBoost, StatsModels, LightGBM, CatBoost, PyBrain, Eli5, fast.ai, TFLearn, Lasagne, nolearn, Elephas, Seaborn, Synaptic, KerasJS, NeuroJS