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Software libraries have become an integral part of modern application development processes. Using existing functionality for complex functionality comes with many benefits and allows development teams to focus on unique value that they are providing. This is especially true for complex machine algorithms being used for cutting edge applications today.
In this blog, we’ll review some popular machine learning libraries and discuss their applications.
A machine learning library provides a collection of functions and classes that can be used for preparing data, training models, and scoring results.
Often, research leads to new methods for prediction, optimization, or other machine learning methods. These methods are then implemented in code in various libraries, sometimes based on code provided by the researchers themselves.
Often code samples can be taken directly from research papers, but this code is often single purpose, sparsely deployed, and not necessarily tested in varying scenarios and therefore would not be defined as a library. The library community is essential for packaging this new research based on need and making it widely available and consistent.
There are many libraries available today for machine learning, each with its own strengths and specializations.
R is actually its own language designed for statistical computing and graphics. Until recently, it was the most popular language or library to use for machine learning. It provides comprehensive coverage of many algorithms, statistical techniques, tests, time-series, grouping, and classification.
While it isn’t specifically for statistics or machine learning, Python has many “packages” available for working on these kinds of projects. Some of the individual packages available include SciPy, NumPy, and Pandas. Python has become the most popular language for machine learning applications.
Developed by Google, Tensorflow is now one of the most popular libraries for use in AI/ML applications. It has a special focus on deep neural networks and has a special data processing model that leads to its name. Tensorflow is available under the Apache 2.0 license which is more suitable to commercial applications.
This is a specialized library designed for Neural Network applications and has a C++ API. This one is open source under the GPL license. OpenNN claims to be faster at loading and training on large data sets than many of the other library options.
Python Machine Learning libraries published Scikit Learn under the 3-clause BSD license.
A Python implementation of the Torch machine learning library. Torch was a library that popularized the idea of using Tensors, or multi-dimensional arrays, for machine learning.
IMSL provides a comprehensive set of Machine Learning tools and algorithms. IMSL is a commercial library that comes with enterprise level support, extensive testing, and integration, as well as thousands of deployments into real applications that have been running for years.
When considering which library to use for your project, there are several things to take into consideration. What programming languages is your application written in? What specific programming language expertise does your team have? Are you building something to sell or distribute, or are you trying to improve internal processes/applications? Is the library going to be used once for a specific purpose, or are you trying to generally improve the overall machine learning capabilities of your offering?
If you are working with very large datasets, consider using C++ or C for performance reasons. If you have a very specific problem, consider finding a specialized solution for that problem. Most applications today will have opportunities to incorporate more and more machine learning. Investing in a standard machine learning library with a breadth of functionality will help your development team not only develop immediate innovation targets, but also help them to get familiar with additional functionality that could be leveraged for use within a project.
Financial Markets risk analysis is one important application that has been implemented in R. Combining R’s statistical analysis with visualizations provides both the quantifiable risk for securities or regions and the visual representation of how this risk changes over time or is affecting particular regions. Some related R functionality used for analysis includes performance analysis, exposure analysis, Monte-Carlo, and visualization.
Most people are familiar with the basic translating tool provided by Google. Underneath, this tool uses Tensorflow to decode and translate words and sentences provided by the user. This use case requires performance, flexibility, and continuous learning as more complex language is included in translation requests. Feedback mechanisms within the tool allow the models to keep getting better with additional use.
A popular use for IMSL algorithms is for options pricing. Historically, many financial institutions used the Black-Scholes model for this process. Now, there is research being done on more advanced models including neural networks to help identify and predict volatility as well as reduce risk. This is a highly profitable field, and there are many different approaches being pursued. IMSL’s high performance, accuracy, and depth of functionality make it a great choice for this kind of research.
IMSL provides a comprehensive set of Machine Learning tools and algorithms including Support Vector Machines, Regression Models, and Neural Networks. Besides the algorithms used for machine learning, IMSL also provides tools for data preparation, statistical analysis, feature selection, and model performance.
With four different language implementations, IMSL provides flexibility for development teams to choose what suits them best. The Fortran and C implementations are fast native implementations while the Java and Python versions provide convenient APIs.
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