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A Plethora of Tools for Machine Learning

When it comes to training computers to act without being explicitly programmed there exist an abundance of tools from the field of Machine Learning. Academics and industry professionals use these tools for building a number of applications from Speech Recognition to Cancer Detection in MRI scans. Many of these tools exist freely available on the web. If you’re interested I have compiled a ranking of these (see the bottom of this page) along with an outline of some important features for differentiating between them. Specifically, the language the tool is written in, a description taken from the home website for each tool, the focus towards a particular paradigm in Machine Learning and some notable uses in academia and industry.

Researchers may use many different libraries at a time, write their own, or not cite any particular tool, so quantifying the relative adoption of each is difficult. Instead a Search Rank is given reflecting the comparative magnitude of Google searches for each tool in May. The score is not necessarily reflective of wide spread adoption but gives us a good indication of which are being used. Note* vague names like “Caffe” were evaluated as “Caffe Machine Learning” to be less ambiguous.

machine learning photo

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I have included a distinction between two Machine Learning sub-fields Deep and Shallow Learning which has become an important split over the last couple of years. Deep Learning is responsible for record results in Image Classification and Voice Recognition and is thus being spearheaded by large data companies like Google, Facebook, and Baidu. Conversely, Shallow Learning methods include a variety of less cutting edge Classification, Clustering and Boosting techniques like Support Vector Machines. Shallow learning methods are still widely in use in fields such as Natural Language Processing, Brain Computer Interfacing, and Information Retrieval.

Detailed Comparison of Machine Learning Packages and Libraries

This table also includes information about the particular tools support for working with a GPU. GPU interfacing has become an important feature for Machine Learning tools because it can accelerate large scale matrix operations. The importance of this to Deep learning methods is apparent. For instance At the GPU Tech Conference in early May 2015 39 of 45 talks given under Machine Learning were about GPU accelerated-Deep Learning applications, these came from 31 major tech companies and 8 universities. The appeal reflects the massive speed improvements in GPU assisted training of Deep Networks and is thus an important feature.

Information about the tools ability to distribute computation across clusters through Hadoop or Spark is also given. This has become an important talking point for Shallow Learning techniques which suit distributed computation. Likewise distributed computation for Deep Networks has also become a talking point as new techniques have been developed for distributed training algorithms.

Lastly some additional notes are attached about the varying use of these tools in academia and industry. What information exists was gathered by searching Machine Learning publications, presentations and distributed code. Some information was also supported by researchers at Google, Facebook and Oracle so many thanks to Greg Mori, Adam Pocock, and Ronan Collobert.

The results of this research show that there are a number of tools being used at the current moment and that it is not yet quite certain which will win the lions share of use in industry or across academia.

Search Rank Tool Language Type Description “quote” Use GPU acceleration Distributed computing Known Academic use Known Industry Use
100 Theano Python Library Numerical computation library for multi-dimensional arrays efficiently Deep and shallow Learning CUDA and Open CL, cuDNN Not Yet Geoffrey Hinton, Yoshio Bengio, and Yann Le Cunn Facebook, Oracle,Google and Parallel Dots
78 Torch 7 Lua Framework Scientific computing framework with wide support for machine learning algorithms Deep and shallow Learning CUDA and Open CL, cuDNN Cutorch NYU, LISA LABS,Purdue e-lab, IDIAP Facebook AI Research, Google Deep Mind, certain people at IBM and several smaller companies
64 R R Environment/ Language Functional language and environment for statistics Shallow Learning RPUD HiPLAR
52 LIBSVM Java and C++ Library A Library for Support Vector Machines Support Vector Machines CUDA Not Yet Oracle
34 scikit-learn Python Library Machine Learning in Python Shallow Learning Not Yet Not Yet
28 MLLIB C++, APIs in JAVA, and Python Library/API Apache Spark’s scalable machine learning library Shallow Learning ScalaCL Spark and Hadoop Oracle
24 Matlab Matlab Environment/ Language High-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical analysis Deep and Shallow Learning Parallel Computing Toolbox (not-free not-open source) Distributed Computing Package (not-free not-open source) Geoffrey Hinton, Graham Taylor, other researchers
18 Pylearn2 Python Library Machine Learning Deep Learning CUDA and Open CL, cuDNN Not Yet LISA LABS
14 VowPal Wabbit C++ Library Out-of-core learning system Shallow Learning CUDA Not Yet Sponsored by Microsoft Research and (previously) Yahoo! Research
13 Caffe C++ Framework Deep learning framework made with expression, speed, and modularity in mind Deep Learning CUDA and Open CL, cuDNN Not Yet Virginia Tech, UC Berkley, NYU Flicker, Yahoo, and Adobe
11 LIBLINEAR Java and C++ Library A Library for Large Linear Classification Support Vector Machines and Logistic Regression CUDA Not Yet Oracle
6 Mahout Java Environment/ Framework An environment for building scalable algorithms Shallow Learning JCUDA Spark and Hadoop
5 Accord.NET .Net Framework Machine learning Deep and Shallow Learning CUDA.net Not Yet
5 NLTK Python Library Programs to work with human language data Text Classification Skits.cuda Not Yet
4 Deeplearning4j Java Framework Commercial-grade, open-source, distributed deep-learning library Deep and shallow Learning JClubas Spark and Hadoop
4 Weka 3 Java Library Collection of machine learning algorithms for data mining tasks Shallow Learning Not Yet distributedWekaSpark
4 MLPY Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
3 Pandas Python Library Data analysis and manipulation Shallow Learning Skits.cuda Not Yet
1 H20 Java, Python and R Environment/ Language open source predictive analytics platform Deep and Shallow Learning Not Yet Spark and Hadoop
0 Cuda-covnet C++ Library machine learning library for neural-network applications Deep Neural Networks CUDA coming in Cuda-covnet2
0 Mallet Java Library Package for statistical natural language processing Shallow Learning JCUDA Spark and Hadoop
0 JSAT Java Library Statistical Analysis Tool Shallow Learning JCUDA Spark and Hadoop
0 MultiBoost C++ Library Machine Learning Boosting Algorithms CUDA Not Yet
0 Shogun C++ Library Machine Learning Shallow Learning CUDA Not Yet
0 MLPACK C++ Library Machine Learning Shallow Learning CUDA Not Yet
0 DLIB C++ Library Machine Learning Shallow Learning CUDA Not Yet
0 Ramp Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
0 Deepnet Python Library GPU-based Machine Learning Deep Learning CUDA Not Yet
0 CUV Python Library GPU-based Machine Learning Deep Learning CUDA Not Yet
0 APRIL-ANN Lua Library Machine Learning Deep Learning Not Yet Not Yet
0 nnForge C++ Framework GPU-based Machine Learning Convolutional and fully-connected neural networks CUDA Not Yet
0 PYML Python Framework Object oriented framework for machine learning SVMs and other kernel methods Skits.cuda Not Yet
0 Milk Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
0 MDP Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
0 Orange Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
0 PYMVPA Python Library Machine Learning Only Classification Skits.cuda Not Yet
0 Monte Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
0 RPY2 Python to R API Low-level interface to R Shallow Learning Skits.cuda Not Yet
0 NueroLab Python Library Machine Learning Feed Forward Neural Networks Skits.cuda Not Yet
0 PythonXX Python Library Machine Learning Shallow Learning Skits.cuda Not Yet
0 Hcluster Python Library Machine Learning Clustering Algorithms Skits.cuda Not Yet
0 FYANN C Library Machine Learning Feed Forward Neural Networks Not Yet Not Yet
0 PyANN Python Library Machine Learning Nearest Neighbours Classification Not Yet Not Yet
0 FFNET Python Library Machine Learning Feed Forward Neural Networks Not Yet Not Yet

Help us Make the Bridge to Neuromemristive Processors

Knowm Inc is focused on the development of neuromemristive processors like kT-RAM. As machine learning pioneers like Geoffrey Hinton know only too well, machine learning is fundamentally related to computational power. We call it the adaptive power problem, and to solve it we need new tools to usher in the next wave of intelligent machines. While GPUs have (finally!) enabled us to demonstrate learning algorithms that approach human-levels on some tasks, they are still a million to a billion times less energy and space efficient than biology. We are taking that gap to zero.

We are interested to know what packages, frameworks and algorithms people solving real-world machine learning problems find most useful so we can focus our effort an build a bridge to kT-RAM and the KnowmAPI. Let us know by leaving a comment below or by contacting us.

Misc. References

  1. Bryan Catanzaro Senior Researcher, Baidu” Speech: The Next Generation” 05/28/2015  Talk given @ GPUTech conference 2015
  2. Dhruv Batra CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service” 05/28/2015 Talk given @ GPUTech conference 2015
  3. Dilip Patolla. “A GPU based Satellite Image Analysis Tool”  05/28/2015 Talk given @ GPUTech conference 2015
  4. Franco Mana. “A High-Density GPU Solution for DNN Training” 05/28/2015 Talk given @ GPUTech conference 2015</a
  5. Hailin Jin. “Collaborative Feature Learning from Social Media” 05/28/2015 Talk given @ GPUTech conference 2015
  6. Noel, Cyprian & Simon Osindero. “S5552 – Transparent Parallelization of Neural Network Training” 05/28/2015  Talk given @ GPUTech conference 2015
  7. Rob Fergus. “S5581 – Visual Object Recognition using Deep Convolution Neural Networks” 05/28/2015  Talk given @ GPUTech conference 2015
  8. Rodrigo Benenson ” Machine Learning Benchmark Results: MNIST” 05/28/2015
  9. Rodrigo Benenson ” Machine Learning Benchmark Results: CIFAR” 05/28/2015
  10. Tom Simonite “Baidu’s Artificial-Intelligence Supercomputer Beats Google at Image Recognition” 05/28/2015
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Jacob Steeves

Jacob Steeves

A bit about myself. I'm the kind of person that loves to talk about interesting ideas and get to work solving mysteries. At core, I'm an investigatory skeptic, a patient problem solver and a mediocre artist. Those green forests, great tunes, and kind people are my favourite things.

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