# Pop!\_OS Machine Learning Kit

## Hardware

- System76 Oryx Pro - https://system76.com/laptops/oryx
- Hardware with NVIDIA GPU 

## Packages

- Tensorflow - https://www.tensorflow.org/
- Keras - https://keras.io/ 
- PyTorch - https://pytorch.org/
- Caffe - http://caffe.berkeleyvision.org/ Caffe2 - https://caffe2.ai/
- 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)

## Example Project
