Five Most Popular Open Source Frameworks Used in Machine Learning

by April 14, 2018 0 comments

Machine language a branch of artificial intelligence which enables system the ability to learn from data without being programmed. Machine learning got evolved from pattern recognition and computational learning theory in artificial intelligence. It has revolutionized the conventional way through developing algorithms that can learn and make predictions on data. There are innumerable factors that have improved the contribution of machine learning. Open source frameworks are one of the major reasons for the boost in machine learning. A framework is a collection of programs, libraries and languages evolved to use in application development. A library is a collection of objects or methods used by the applications which avoid rewriting of same codes.

The article lists five most popular frameworks that significantly help data scientists and engineers in their big data analytics journey.


1. Tensorflow

Tensorflow is an open source data software library for data programming used in a wide range of applications. It is mainly used for perceptual and language understanding tasks. It has a Python-based interface to conduct research on machine learning and deep neural networks. Google Brain team developed Tensorflow and has incorporated it in many Google products, speech recognition, Gmail, and photos. Also, it is heavily used for research and production at Google. It was initially launched under Apache 2.0 open source and is now available on 64-bit LinuxmacOSWindows, and mobile computing platforms including Android and iOS.


2. Amazon Machine Learning

Amazon Machine Learning is a platform that makes it easy for developers of all skill level to deal with machine learning. Visualisation tools and wizards offered by AML helps to create models without having the need to learn complex algorithms and technology. Once the model is done, AML makes predictions using simple APIs. The advantage of AML is that it can generate billions of predictions daily and serve those predictions in real-time at a high rate. Predictions can be developed with the help of data available from Amazon Redshift.


3. Accor.NET

Accor.NET is a .NET machine learning framework written in C#. It entwines both audio and image processing libraries. It is highly useful in signal processing, statistics application, and computer audition. Accord handles pattern recognition, image and signal processing for linear algebra, statistical data processing and more. It offers 40 different statistical distributions, 30 hypothetical tests, and 38 kernel functions.


4. Shogun

Shogun is an open source machine learning software written in C++. It deploys numerous algorithms and data structures for machine learning problems. Shogun gives interfaces for MATLABOctavePythonRJavaLua and Ruby. It is also helpful in binding to other machine learning libraries. It mainly includes SVMLight, LibSVM, libqp, SLEP, LibLinear, VowpalWabbit and Tapkee.


 5. Apache Signa, Apache Mahout and Apache Spark MLib

Apache Signa is an open source machine learning library. It provides an easy architecture for scalable distributed training and is mainly used for natural language processing and image recognition compatible with a range of hardware. On the other hand, Apache Mahout is a distributed linear algebraic framework. It helps to produce a free implementation of distributed ML algorithms which focuses on collaborative filtering, clustering and classification. Mahout is useful in Java libraries and java collection comprising of various kinds of mathematical operations. Spark MLib is developed with an objective to make machine learning easy. It puts many learning algorithms, utilities including classification and clustering under one umbrella.

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