A Lap Around Some Machine Learning Frameworks

Below I have highlighted some of the commonly used, open source, machine learning frameworks you will find in use today.


  • Deep learning framework created by the Google Brain Team
  • Started out as a proprietary ML system based on deep neural networks at Google
  • Can run on CPUs and GPUs.
  • Focuses on graph based computations - used in neural networks
  • Has Python support and a C api.
  • Check out samples here
  • Check it out here

Microsoft Cognitive Toolkit

  • Deep learning framework created by Microsoft Research
  • Formerly known as CNTK
  • Creates neural networks via directed graphs.
  • Works on CPU and GPU
  • Works with Python as well as C#, Java and C++
  • Significantly faster in some circumstances than other frameworks
  • Check out examples here
  • Check it out here


  • Python based numerical computational library
  • Developed primarily by the ML group at Montreal University
  • Major development will cease by end of year due to the evolving ecosystem and stronger players their own libraries.
  • Check it out here


  • Created by Ronan Collobert, Koray Kavukcuoglu and Clement Farabet.
  • Uses Lua as the scripting language
  • Focus is on GPU computations
  • Has neural network capabilities as well as support for popular optimization libraries
  • Large set of samples and good community.
  • Google’s DeepMind used Torch up until a year ago when they transitioned to TensorFlow
  • Check it out here


  • Deep learning framework developed by UC Berkley
  • Models are created via configuration (vs. coding) making it potentially easier to create models;
  • It is very fast - example it can process 60m images per day on a single NVIDA K80 GPU
  • Extensible code and decent community
  • Check it out here


  • Built by Facebook - an extension to Caffe
  • Aims for ML in production especially on mobile devices as well as large scale deployments
  • Has these improvements over Caffe
    • first-class support for large-scale distributed training
    • mobile deployment
    • new hardware support (in addition to CPU and CUDA)
    • flexibility for future directions such as quantized computation
    • stress tested by the vast scale of Facebook applications
  • Check it out here


  • Created by François Chollet
  • Neural network library written in Python.
  • Can run on top of several different frameworks (e.g. Tensorflow, Microsoft CNTK)
  • Keras is more an abstraction layer over underlying frameworks making it very easy to create and configure a neural network regardless of the backend library.
  • Check it out here

Apache Spark Mllib

  • Built on top of apache spark - an open source cluster-computing framework leveraging memory over disk i/o for far superior performance over frameworks like Hadoop
  • 2 packages - MLLib and ML
  • ML provides higher level api over dataframes but does not have all the algorithms that MLLib has
  • Can be as 9x fast as disk based Mahout
  • Includes many common machine learning algorithms
  • Check it out here

Apache Mahout

  • Mahout means Elephant Rider.
  • Uses Samsara, a vector math experimentation environment with R-like syntax which works at scale
  • Previously, Amazon used it to for recommendations
  • Sits on top of MapReduce and is fairly mature but constrained by disk i/o -slow and not good with intensive jobs. Work is underway to move to Spark.
  • Focuses on collaborative filtering, clustering and classification
  • Check it out here