Pop!\_OS Deep Learning

Packages

• Tensorman - https://support.system76.com/articles/use-tensorman/
• Tensorflow - https://www.tensorflow.org/
• Keras - https://keras.io/
• PyTorch - https://pytorch.org/
• Theano - needs packaging - http://www.deeplearning.net/software/theano/
• Caffe - http://caffe.berkeleyvision.org/ Caffe2 - https://caffe2.ai/
• Caffe 2 -
• Microsoft Cognitive Toolkit - https://www.microsoft.com/en-us/cognitive-toolkit/
• Deeplearning 4j - https://deeplearning4j.org/
• ELKI - https://elki-project.github.io/
• H20 - https://github.com/h2oai/h2o-3
• Apache Mahout - https://mahout.apache.org/
• Apache MXNet - https://mxnet.apache.org/
• Apache Spark - https://github.com/apache/spark
• OpenNN - http://www.opennn.net/
• Mallet - http://mallet.cs.umass.edu/
• mlpack - http://mlpack.org/
• MOA - https://github.com/waikato/moa
• Orange - https://orange.biolab.si/
• SciKit-Learn - https://github.com/scikit-learn/scikit-learn
• Shogun - https://github.com/shogun-toolbox/shogun
• Yooreeka - https://github.com/marmanis/yooreeka
• Weka - https://www.cs.waikato.ac.nz/~ml/weka/
• Eigen - http://eigen.tuxfamily.org/index.php?title=Main_Page
• MATLAB - https://www.mathworks.com/help/matlab/ref/matlablinux.html I'm not sure how viable this is (licensing, etc.) but per a customer it is standardized in the field
• KNIME - https://en.wikipedia.org/wiki/KNIME needs evaluation (partially proprietary)
• RapidMiner - https://github.com/rapidminer/ needs evaluation (partially proprietary)
• Conda
• Miniconda