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Survey on Recent Machine Learning Tools, Platforms and Interface INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 Survey on Recent Machine learning Tools, Platforms and Interface 1.Mr.S.Karthi, M.E,AP/IT, St.Joseph College of Engineering, Chennai, [email protected] 2.Mrs.R.Raja Ramya, M.E.(Ph.D),AP/IT, Saveetha Engineering College, Chennai, ramyanitha @ gmail.com 3.Mrs.N.Kanagavalli, M.E.(Ph.D),AP/IT, Saveetha Engineering College, Chennai, [email protected] ABSTRACT- Machine learning and deep in the process of working through a machine learning are recent booming research areas. Tools are learning problem a big part of machine learning and choosing the right tool can be as important as working with the Why Use Tools? best algorithms. So that the new researchers are not aware of the tools and frameworks are helped to Machine learning tools make applied machine machine learning. There are lots of open source tools learning faster, easier and more fun. and frameworks are available in machine learning and deep learning. In this paper, various open source Faster: This means that the time from ideas to machine learning tools and frameworks are explained. results is greatly shortened. The alternative is that you have to implement each capability Keywords-open source tools, frameworks, platforms yourself. Introduction Easier: The open source tools are very easier to learn by individual. Based on the input, we A programming tool or software development tool is form the output in an easiest way. a computer program that software developers use to create, debug, maintain, or otherwise support other This requires research, deeper exercise in order programs and applications. to understand the techniques, and a higher level The term usually refers to relatively simple programs, of engineering to ensure it is implemented that can be combined together to accomplish a task, efficiently. much as one might use multiple hand tools to fix a physical object. Fun: Learning open source tools are like playing a game. The tools are really helped to The most basic tools are a source code editor and beginners to get good results. You can use the a compiler or interpreter, which are used ubiquitously extra time to get better results or work on more and continuously. Other tools are used more or less projects. depending on the language, development methodology, and individual engineer, and are often Good versus Great Tools used for a discrete task, like a debugger or profiler. You want to use the best tools for the problems that Machine learning engineers are part of the you are working on. How to do tell the difference engineering team who build the product and the between good and great machine learning tools? algorithms, making sure that it works reliably, quickly, and at-scale. They work closely with Intuitive Interface: Great machine learning data scientist to understand the theoretical and tools provide an intuitive interface onto the sub- business aspect of it. tasks of the applied machine learning process. There’s a good mapping and suitability in the Machine learning tools are not just interface for the task. implementations of machine learning algorithms. They can be, but they can also provide capabilities that you can use at any step Volume 6, Issue 6, June 2019 673 http://ijics.com INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 Best Practice: Great machine learning tools A platform provides all you need to run a embody best practices for process, project, whereas a library only provides discrete configuration and implementation capabilities or parts of what you need to complete a project. Examples include automatic configuration of machine learning algorithms and good process This is not a perfect distinction because some built into the structure of the tool. machine learning platforms are also libraries or some libraries provide a graphical user Trusted Resource: Great machine learning interface. tools are well maintained, updated frequently and have a community of people around it. List of frameworks for machine When to Use Machine Learning Tools learning Machine learning tools can save our time and help you consistency deliver good results across 1. Apache Singa is a general distributed deep projects. Some examples of when you may get learning platform for training big deep the most benefit from using machine learning learning models over large datasets. It is tools include: designed with an intuitive programming model based on the layer abstraction. Getting Starting: A variety of popular deep learning models are When you are just getting started machine supported, namely feed-forward models learning tools guide you through the process of including convolutional neural networks delivering good results quickly and give you (CNN), energy models like restricted confidence to continue on with your next Boltzmann machine (RBM), and recurrent project. neural networks (RNN). Many built-in layers are provided for users. Day-to-Day: When you need to get good results to a question quickly machine learning 2. Amazon Machine Learning is a service tools can allow you to focus on the specifics of that makes it easy for developers of all skill your problem rather than on the depths of the levels to use machine learning technology. techniques you need to use to get an answer. Amazon Machine Learning provides visualization tools and wizards that guide Project Work: When you are working on a you through the process of creating machine large project, machine learning tools can help learning (ML) models without having to you to prototype a solution, figure out the learn complex ML algorithms and requirements and give you a template for the technology. system that you may want to implement. It connects to data stored in Amazon S3, Platforms versus Libraries Redshift, or RDS, and can run binary There are a lot of machine learning tools. classification, multiclass categorization, or Enough that a google search can leave you regression on said data to create a model. feeling overwhelmed. One useful way to think about machine learning tools it so separate them 3. Azure ML Studio allows Microsoft Azure into Platforms and Libraries. users to create and train models, then turn them into APIs that can be consumed by other services. Volume 6, Issue 6, June 2019 674 http://ijics.com INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 extend the platform seamlessly into your Users get up to 10GB of storage per account Hadoop environments. for model data, although you can also connect your own Azure storage to the 7. Massive Online Analysis (MOA) is the service for larger models. A wide range of most popular open source framework for algorithms are available, courtesy of both data stream mining, with a very active Microsoft and third parties. growing community. You don’t even need an account to try out It includes a collection of machine learning the service; you can log in anonymously algorithms (classification, regression, and use Azure ML Studio for up to eight clustering, outlier detection, concept drift hours. detection and recommender systems) and tools for evaluation. Related to the WEKA 4. Caffe is a deep learning framework made project, MOA is also written in Java, while with expression, speed, and modularity in scaling to more demanding problems. mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by 8. MLlib (Spark) is Apache Spark’s machine community contributors. learning library. Its goal is to make practical machine learning scalable and easy. 5. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released It consists of common learning algorithms under the BSD 2-Clause license. Models and utilities, including classification, and optimization are defined by regression, clustering, collaborative configuration without hard-coding & user filtering, dimensionality reduction, as well can switch between CPU and as lower-level optimization primitives and GPU. Speed makes Caffe perfect for higher-level pipeline APIs. research experiments and industry deployment. Caffe can process over 60M 9. mlpack, a C++-based machine learning images per day with a single NVIDIA K40 library originally rolled out in 2011 and GPU. designed for “scalability, speed, and ease- of-use,” according to the library’s creators. 6. H2O makes it possible for anyone to easily Implementing mlpack can be done through apply math and predictive analytics to solve a cache of command-line executable for today’s most challenging business quick-and-dirty, “black box” operations, or problems. with a C++ API for more sophisticated work. It intelligently combines unique features not mlpack provides these algorithms as simple currently found in other machine learning command-line programs and C++ classes platforms including: Best of Breed Open which can then be integrated into larger- Source Technology, Easy-to-use WebUI scale machine learning solutions. and Familiar Interfaces, Data Agnostic Support for all Common Database and File 10. Pattern is a web mining module for the Types. Python programming language. With H2O, you can work with your existing It has tools for data mining (Google, Twitter languages and tools. Further, you can and Wikipedia API, a web crawler, a Volume 6, Issue 6, June 2019 675 http://ijics.com INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 HTML DOM parser), natural language Graphs can be assembled with C++ or Python processing (part-of-speech taggers, n-gram and can be processed on CPUs or GPUs. search, sentiment analysis, WordNet), machine learning (vector space model, 14. Theano is a Python library that lets you to clustering, SVM), network analysis and define, optimize, and evaluate mathematical <canvas> visualization. expressions, especially ones with multi- dimensional arrays (numpy.ndarray). 11. Scikit-Learn leverages Python’s breadth by building on top of several existing Python Using Theano it is possible to attain speeds packages — NumPy, SciPy, and matplotlib rivaling hand-crafted C implementations for — for math and science work.
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