Apache SystemML
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Apache SystemML
Apache SystemDS (Previously, Apache SystemML) is an open source ML system for the end-to-end data science lifecycle. SystemDS's distinguishing characteristics are: # Algorithm customizability via R-like and Python-like languages. # Multiple execution modes, including Standalone, Spark Batch, Spark MLContext, Hadoop Batch, and JMLC. # Automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability. History SystemML was created in 2010 by researchers at the IBM Almaden Research Center led by IBM Fellow Shivakumar Vaithyanathan. It was observed that data scientists would write machine learning algorithms in languages such as R and Python for small data. When it came time to scale to big data, a systems programmer would be needed to scale the algorithm in a language such as Scala. This process typically involved days or weeks per iteration, and errors would occur translating the algorithms to operate on big data. SystemML seeks to simpli ...
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Apache SystemDS Logo
The Apache () are a group of culturally related Native American tribes in the Southwestern United States, which include the Chiricahua, Jicarilla, Lipan, Mescalero, Mimbreño, Ndendahe (Bedonkohe or Mogollon and Nednhi or Carrizaleño and Janero), Salinero, Plains (Kataka or Semat or "Kiowa-Apache") and Western Apache ( Aravaipa, Pinaleño, Coyotero, Tonto). Distant cousins of the Apache are the Navajo, with whom they share the Southern Athabaskan languages. There are Apache communities in Oklahoma and Texas, and reservations in Arizona and New Mexico. Apache people have moved throughout the United States and elsewhere, including urban centers. The Apache Nations are politically autonomous, speak several different languages, and have distinct cultures. Historically, the Apache homelands have consisted of high mountains, sheltered and watered valleys, deep canyons, deserts, and the southern Great Plains, including areas in what is now Eastern Arizona, Northern Mexico (Sono ...
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R (programming Language)
R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. Users have created packages to augment the functions of the R language. According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages used in data mining. R ranks 12th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020. The official R software environment is an open-source free software environment within the GNU package, available under the GNU General Public License. It is written primarily in C, Fortran, and R itself (partially self-hosting). Precompiled executables are provided for various operating systems. R ...
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Java Platform
Java is a set of computer software and specifications developed by James Gosling at Sun Microsystems, which was later acquired by the Oracle Corporation, that provides a system for developing application software and deploying it in a cross-platform computing environment. Java is used in a wide variety of computing platforms from embedded devices and mobile phones to enterprise servers and supercomputers. Java applets, which are less common than standalone Java applications, were commonly run in secure, sandboxed environments to provide many features of native applications through being embedded in HTML pages. Writing in the Java programming language is the primary way to produce code that will be deployed as byte code in a Java virtual machine (JVM); byte code compilers are also available for other languages, including Ada, JavaScript, Python, and Ruby. In addition, several languages have been designed to run natively on the JVM, including Clojure, Groovy, and Scala. J ...
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Software Using The Apache License
Software is a set of computer programs and associated documentation and data. This is in contrast to hardware, from which the system is built and which actually performs the work. At the lowest programming level, executable code consists of machine language instructions supported by an individual processor—typically a central processing unit (CPU) or a graphics processing unit (GPU). Machine language consists of groups of binary values signifying processor instructions that change the state of the computer from its preceding state. For example, an instruction may change the value stored in a particular storage location in the computer—an effect that is not directly observable to the user. An instruction may also invoke one of many input or output operations, for example displaying some text on a computer screen; causing state changes which should be visible to the user. The processor executes the instructions in the order they are provided, unless it is instructed ...
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Apache Software Foundation Projects
The Apache () are a group of culturally related Native American tribes in the Southwestern United States, which include the Chiricahua, Jicarilla, Lipan, Mescalero, Mimbreño, Ndendahe (Bedonkohe or Mogollon and Nednhi or Carrizaleño and Janero), Salinero, Plains (Kataka or Semat or "Kiowa-Apache") and Western Apache ( Aravaipa, Pinaleño, Coyotero, Tonto). Distant cousins of the Apache are the Navajo, with whom they share the Southern Athabaskan languages. There are Apache communities in Oklahoma and Texas, and reservations in Arizona and New Mexico. Apache people have moved throughout the United States and elsewhere, including urban centers. The Apache Nations are politically autonomous, speak several different languages, and have distinct cultures. Historically, the Apache homelands have consisted of high mountains, sheltered and watered valleys, deep canyons, deserts, and the southern Great Plains, including areas in what is now Eastern Arizona, Northern Mexico (Son ...
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Hadoop
Apache Hadoop () is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework. The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This appro ...
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Data Mining And Machine Learning Software
In the pursuit of knowledge, data (; ) is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and in virtually every other form of human organizational activity. Examples of data sets include price indices (such as consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the raw facts and figures which can be used in such a manner in order to capture the useful information out of it. Dat ...
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Cluster Computing
A computer cluster is a set of computers that work together so that they can be viewed as a single system. Unlike grid computers, computer clusters have each node set to perform the same task, controlled and scheduled by software. The components of a cluster are usually connected to each other through fast local area networks, with each node (computer used as a server) running its own instance of an operating system. In most circumstances, all of the nodes use the same hardware and the same operating system, although in some setups (e.g. using Open Source Cluster Application Resources (OSCAR)), different operating systems can be used on each computer, or different hardware. Clusters are usually deployed to improve performance and availability over that of a single computer, while typically being much more cost-effective than single computers of comparable speed or availability. Computer clusters emerged as a result of convergence of a number of computing trends including t ...
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Comparison Of Deep Learning Software
The following table compares notable software frameworks, libraries and computer programs for deep learning. Deep-learning software by name Comparison of compatibility of machine learning models See also *Comparison of numerical-analysis software *Comparison of statistical packages *List of datasets for machine-learning research *List of numerical-analysis software Listed here are notable end-user computer applications intended for use with numerical or data analysis: Numerical-software packages General-purpose computer algebra systems Interface-oriented Language-oriented Historically signific ... References {{reflist, 33em * Deep learning frameworks ...
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Euclidean Distance
In mathematics, the Euclidean distance between two points in Euclidean space is the length of a line segment between the two points. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefore occasionally being called the Pythagorean distance. These names come from the ancient Greek mathematicians Euclid and Pythagoras, although Euclid did not represent distances as numbers, and the connection from the Pythagorean theorem to distance calculation was not made until the 18th century. The distance between two objects that are not points is usually defined to be the smallest distance among pairs of points from the two objects. Formulas are known for computing distances between different types of objects, such as the distance from a point to a line. In advanced mathematics, the concept of distance has been generalized to abstract metric spaces, and other distances than Euclidean have been studied. In some applications in statistic ...
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DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. It is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature. In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, ACM SIGKDD. , the follow-up paper "DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN" appears in the list of the 8 most downloaded articles of the prestigious ACM Transactions on Database System ...
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Principal Component Analysis
Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where th ...
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