Machine Learning algorithms and libraries overview

Nice brief overview of some Machine Learning algorithms highlighting their strengths and weaknesses.
Big 3 machine learning tasks, which are by far the most common ones. They are:

1.     Regression
2.     Classification
3.     Clustering

Details:
https://elitedatascience.com/machine-learning-algorithms

Here are also some observations on the top five characteristics of ML libraries that developers should consider when deciding what library to use:

Programming paradigm

• Symbolic: Spark MLlib, MMLSpark, BigDL, CNTK, H2O.ai, Keras, Caffe2
• Imperative: scikit-learn, auto sklearn, TPOT, PyTorch
• Hybrid: MXNet, TensorFlow

Machine learning algorithms

• Supervised and unsupervised: Spark MLlib, scikit-learn, H2O.ai, MMLSpark, Mahout
• Deep learning: TensorFlow, PyTorch, Caffe2 (image), Keras, MXNet, CNTK, BigDL, MMLSpark (image and text), H2O.ai (via the deepwater plugin)
• Recommendation system: Spark MLlib, H2O.ai (via the sparkling-water plugin), Mahout

Hardware and performance

• CPU: Spark MLlib, scikit-learn, auto sklearn, TPOT, BigDL
• GPU: Keras, PyTorch, Caffe2, MMLSpark, H2O.ai, Mahout, CNTK, MXNet, TensorFlow
• Mobile Computing: MXNet, TensorFlow, Caffe2

Details:
https://www.oreilly.com/ideas/square-off-machine-learning-libraries