Comprehensive Survey of Machine Learning Tools and Libraries
This article presents a detailed overview and ranking of numerous machine learning tools and libraries, distinguishing deep and shallow learning approaches, highlighting language support, GPU acceleration, and distributed computing capabilities, and provides insights into their academic and industrial usage.
Rich Machine Learning Tools
When training computers to act without explicit programming, a large number of machine‑learning tools are available. Researchers and industry professionals use these tools for applications ranging from speech recognition to cancer detection, and many are freely accessible online.
The ranking shown reflects Google search volume in May, providing an indicator of current interest rather than definitive adoption rates. Tools such as Caffe are listed under “Caffe Machine Learning” to avoid ambiguity.
Overview of Machine Learning Tools
The survey separates the field into Deep Learning and Shallow Learning. Deep Learning, driven by large data companies, excels at image classification and speech recognition, while Shallow Learning includes methods like support‑vector machines that remain widely used in NLP, brain‑computer interfaces, and information retrieval.
A detailed comparison table follows, indicating each tool’s language, type (library, framework, environment), primary use, GPU‑acceleration support (e.g., CUDA, OpenCL, cuDNN), and distributed‑computing capabilities (e.g., Hadoop, Spark).
Search Rank
Tool
Language
Type
Description
Use
GPU acceleration
Distributed computing
100
Theano
Python
Library
Numerical computation library for multi‑dimensional arrays efficiently
Deep and shallow Learning
CUDA and Open CL
cuDNN Cutorch
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
64
R
R
Environment/ Language
Functional language and environment for statistics
Shallow Learning
RPUD
HiPLAR
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