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Author: A.A. Pollicini
Publisher: Springer Science & Business Media
ISBN: 9400908792
Category : Computers
Languages : en
Pages : 344
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Book Description
I am very pleased to write these few brief paragraphs introducing this book, and would like to take this opportunity to attempt to set the Toolpack project in an appropriate historical context. The Toolpack project must be considered to have actually began in the Fall of 1978, when Prof. Webb C. Miller, at a meeting at Jet Propulsion Laboratories in Pasadena, California, suggested that there be a large-scale project, called Toolpack, aimed at pulling together a comprehensive collection of mathematical software development tools. It was suggested that the project follow the pattern of other "Pack" projects, such as Eispack, Linpack, and Funpack which had assembled and systematized comprehensive collections of mathematical software in such areas as eigenvalue computation, linear equation solution and special function approximation. From the that the Toolpack project would differ significantly from beginning it was recognized these earlier "Pack" projects in that it was attempting to assemble and systematize software in an area which was not well established and understood. Thus it was not clear how to organize and integrate the tools we were to collect into Toolpack. As a consequence Toolpack became simultaneously a research project and a development project. The research was aimed at determining effective strategies for large-scale integration of large-scale software tools, and the development project was aimed at implementing these strategies and using them to put high quality tools at the disposal of working mathematical software writers.
Author: A.A. Pollicini
Publisher: Springer Science & Business Media
ISBN: 9400908792
Category : Computers
Languages : en
Pages : 344
Get Book Here
Book Description
I am very pleased to write these few brief paragraphs introducing this book, and would like to take this opportunity to attempt to set the Toolpack project in an appropriate historical context. The Toolpack project must be considered to have actually began in the Fall of 1978, when Prof. Webb C. Miller, at a meeting at Jet Propulsion Laboratories in Pasadena, California, suggested that there be a large-scale project, called Toolpack, aimed at pulling together a comprehensive collection of mathematical software development tools. It was suggested that the project follow the pattern of other "Pack" projects, such as Eispack, Linpack, and Funpack which had assembled and systematized comprehensive collections of mathematical software in such areas as eigenvalue computation, linear equation solution and special function approximation. From the that the Toolpack project would differ significantly from beginning it was recognized these earlier "Pack" projects in that it was attempting to assemble and systematize software in an area which was not well established and understood. Thus it was not clear how to organize and integrate the tools we were to collect into Toolpack. As a consequence Toolpack became simultaneously a research project and a development project. The research was aimed at determining effective strategies for large-scale integration of large-scale software tools, and the development project was aimed at implementing these strategies and using them to put high quality tools at the disposal of working mathematical software writers.
Author: Peter J. L. Wallis
Publisher: Springer Science & Business Media
ISBN: 9783540138785
Category : Computers
Languages : en
Pages : 172
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Book Description
Author: Peri L. Tarr
Publisher: Springer Science & Business Media
ISBN: 3642198236
Category : Computers
Languages : en
Pages : 410
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Book Description
Software engineering research can trace its roots to a few highly influential individuals. Among that select group is Leon J. Osterweil, who has been a major force in driving software engineering from its infancy to its modern reality. For more than three decades, Prof. Osterweil's work has fundamentally defined or significantly impacted major directions in software analysis, development tools and environments, and software process--all critical parts of software engineering as it is practiced today. His exceptional contributions to the field have been recognized with numerous awards and honors through his career, including the ACM SIGSOFT Outstanding Research Award, in recognition of his extensive and sustained research impact, and the ACM SIGSOFT Influential Educator Award, in recognition of his career-long achievements as an educator and mentor. In honor of Prof. Osterweil's profound accomplishments, this book was prepared for a special honorary event held during the 2011 International Conference on Software Engineering (ICSE). It contains some of his most important published works to date, together with several new articles written by leading authorities in the field, exploring the broad impact of his work in the past and how it will further impact software engineering research in the future. These papers, part of the core software engineering legacy and now available in one commented volume for the first time, are grouped into three sections: flow analysis for software dependability, the software lifecycle, and software process.
Author: Raymond C. Houghton
Publisher:
ISBN:
Category : Computer programming
Languages : en
Pages : 212
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Book Description
Author: César Pérz López
Publisher: SCIENTIFIC BOOKS
ISBN:
Category : Computers
Languages : en
Pages : 362
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Book Description
Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. Classification techniques predict categorical responses and Regression techniques predict continuous responses. This book develops Classification Techniques including Classification Support Vector Machine, Decision Trees, Logistic Regression, Discriminant Analysis, Nearest Neighbor Classifiers, Ensemble Classifiers, Naive Bayes, Pattern Recognition and Neural Networks for Classification.
Author: C. Perez
Publisher: CESAR PEREZ
ISBN: 1716877423
Category : Computers
Languages : en
Pages : 322
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Book Description
Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. Among all these tools highlights MATLAB. MATLAB implements various toolboxes for working on big data analytics, such as Statistics Toolbox and Neural Network Toolbox (Deep Learning Toolbox for version 18) . This book develops the work capabilities of MATLAB with Neural Networks and Big Data.
Author: CESAR PERZ LOPEZ
Publisher: SCIENTIFIC BOOKS
ISBN:
Category : Computers
Languages : en
Pages : 200
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Book Description
Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Neural networks and their applications are a fundamental tool to develop work in Artificial Intelligence. On the other hand, unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. Some examples of unsupervised learning algorithms include clustering and association rules. In the case of this type of learning, there is no pre-training data set; the problem is approached blindly and only with logical operations to guide it. Although at first glance it seems impossible, it is about the ability to solve complex problems using only input data and logical algorithms. This avoids the use of reference data. Unsupervised learning algorithms are used to discover hidden patterns in unlabeled data. Unlike supervised learning algorithms, where there is prior knowledge of the desired answers, these algorithms do not have a set of ordered data. They are responsible for determining the most important common characteristics of a group of information and then grouping them according to their similarities. Among the most interesting models are the neural networks. MATLAB implementrs the Deep Learning Toolbox specialized in the techniques of analytics based on neural networks. Throughout this book the techniques of analytics for clustering and classification based on neural networks are developed using MATLAB software
Author:
Publisher:
ISBN:
Category : Fusion
Languages : en
Pages : 112
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Book Description
Author: C. Perez
Publisher: CESAR PEREZ
ISBN: 1716877563
Category : Computers
Languages : en
Pages : 328
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Book Description
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks. This book deeps in big data and deep learning techniques
Author: Cesar Perez Lopez
Publisher: CESAR PEREZ
ISBN: 1716584841
Category : Computers
Languages : en
Pages : 154
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Book Description
MATLAB has the tool Deep Learning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets (Big data), you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.
