Scientific Data Analysis using Jython Scripting and Java

Scientific Data Analysis using Jython Scripting and Java
Author: Sergei V. Chekanov
Publsiher: Springer Science & Business Media
Total Pages: 440
Release: 2010-08-05
ISBN: 1849962871
Category: Computers
Language: EN, FR, DE, ES & NL

Scientific Data Analysis using Jython Scripting and Java Book Excerpt:

Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included. Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.

Scientific Data Analysis Using Jython Scripting and Java

Scientific Data Analysis Using Jython Scripting and Java
Author: Sergei V. Chekanov
Publsiher: Unknown
Total Pages: 466
Release: 2010-09-01
ISBN: 9781849962889
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Scientific Data Analysis Using Jython Scripting and Java Book Excerpt:

Numeric Computation and Statistical Data Analysis on the Java Platform

Numeric Computation and Statistical Data Analysis on the Java Platform
Author: Sergei V. Chekanov
Publsiher: Springer
Total Pages: 620
Release: 2016-03-23
ISBN: 3319285319
Category: Computers
Language: EN, FR, DE, ES & NL

Numeric Computation and Statistical Data Analysis on the Java Platform Book Excerpt:

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.

Neural Information Processing

Neural Information Processing
Author: Tingwen Huang,Zhigang Zeng,Chuandong Li,Chi Sing Leung
Publsiher: Springer
Total Pages: 712
Release: 2012-11-05
ISBN: 364234481X
Category: Computers
Language: EN, FR, DE, ES & NL

Neural Information Processing Book Excerpt:

The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis.

The Handbook On Reasoning based Intelligent Systems

The Handbook On Reasoning based Intelligent Systems
Author: Nakamatsu Kazumi,Jain Lakhmi C
Publsiher: World Scientific
Total Pages: 680
Release: 2013-01-18
ISBN: 9814489166
Category: Computers
Language: EN, FR, DE, ES & NL

The Handbook On Reasoning based Intelligent Systems Book Excerpt:

This book consists of various contributions in conjunction with the keywords “reasoning” and “intelligent systems”, which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.

Performance Tuning of Scientific Applications

Performance Tuning of Scientific Applications
Author: David H. Bailey,Robert F. Lucas,Samuel Williams
Publsiher: CRC Press
Total Pages: 400
Release: 2010-11-23
ISBN: 1439815704
Category: Computers
Language: EN, FR, DE, ES & NL

Performance Tuning of Scientific Applications Book Excerpt:

With contributions from some of the most notable experts in the field, Performance Tuning of Scientific Applications presents current research in performance analysis. The book focuses on the following areas. Performance monitoring: Describes the state of the art in hardware and software tools that are commonly used for monitoring and measuring performance and managing large quantities of data Performance analysis: Discusses modern approaches to computer performance benchmarking and presents results that offer valuable insight into these studies Performance modeling: Explains how researchers deduce accurate performance models from raw performance data or from other high-level characteristics of a scientific computation Automatic performance tuning: Explores ongoing research into automatic and semi-automatic techniques for optimizing computer programs to achieve superior performance on any computer platform Application tuning: Provides examples that show how the appropriate analysis of performance and some deft changes have resulted in extremely high performance Performance analysis has grown into a full-fledged, sophisticated field of empirical science. Describing useful research in modern performance science and engineering, this book helps real-world users of parallel computer systems to better understand both the performance vagaries arising in scientific applications and the practical means for improving performance. Read about the book on HPCwire and insideHPC

Python Scripting for Computational Science

Python Scripting for Computational Science
Author: Hans Petter Langtangen
Publsiher: Springer Science & Business Media
Total Pages: 732
Release: 2013-03-14
ISBN: 3662054507
Category: Computers
Language: EN, FR, DE, ES & NL

Python Scripting for Computational Science Book Excerpt:

Scripting with Python makes you productive and increases the reliability of your scientific work. Here, the author teaches you how to develop tailored, flexible, and efficient working environments built from small programs (scripts) written in Python. The focus is on examples and applications of relevance to computational science: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping programs with graphical user interfaces; making computational Web services; creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran; and building flexible object-oriented programming interfaces to existing C/C++ or Fortran libraries.

Workflows for e Science

Workflows for e Science
Author: Ian J. Taylor,Ewa Deelman,Dennis B. Gannon,Matthew Shields
Publsiher: Springer Science & Business Media
Total Pages: 526
Release: 2007-12-31
ISBN: 184628757X
Category: Computers
Language: EN, FR, DE, ES & NL

Workflows for e Science Book Excerpt:

This is a timely book presenting an overview of the current state-of-the-art within established projects, presenting many different aspects of workflow from users to tool builders. It provides an overview of active research, from a number of different perspectives. It includes theoretical aspects of workflow and deals with workflow for e-Science as opposed to e-Commerce. The topics covered will be of interest to a wide range of practitioners.

Astronomical Data Analysis Software and Systems XIV

Astronomical Data Analysis Software and Systems XIV
Author: Patrick L. Shopbell,Matthew C. Britton,Rick Ebert
Publsiher: Unknown
Total Pages: 709
Release: 2005
ISBN: 1928374650XXX
Category: Astronomy
Language: EN, FR, DE, ES & NL

Astronomical Data Analysis Software and Systems XIV Book Excerpt:

Agile Data Science

Agile Data Science
Author: Russell Jurney
Publsiher: "O'Reilly Media, Inc."
Total Pages: 178
Release: 2013-10-15
ISBN: 1449326927
Category: COMPUTERS
Language: EN, FR, DE, ES & NL

Agile Data Science Book Excerpt:

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Untitled

Untitled
Author: Anonim
Publsiher: IOS Press
Total Pages: 135
Release: 2022
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Untitled Book Excerpt:

New Scientist

New Scientist
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 2005
ISBN: 1928374650XXX
Category: Science
Language: EN, FR, DE, ES & NL

New Scientist Book Excerpt:

Web Services Research for Emerging Applications Discoveries and Trends

Web Services Research for Emerging Applications  Discoveries and Trends
Author: Zhang, Liang-Jie
Publsiher: IGI Global
Total Pages: 720
Release: 2010-02-28
ISBN: 161520685X
Category: Computers
Language: EN, FR, DE, ES & NL

Web Services Research for Emerging Applications Discoveries and Trends Book Excerpt:

"This book provides a comprehensive assessment of the latest developments in Web services research, focusing on composing and coordinating Web services, XML security, and service oriented architecture, and presenting new and emerging research in the Web services discipline"--Provided by publisher.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Peter A. Flach,Tijl De Bie,Nello Cristianini
Publsiher: Springer
Total Pages: 867
Release: 2012-09-11
ISBN: 3642334865
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning and Knowledge Discovery in Databases Book Excerpt:

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Web Microanalysis of Big Image Data

Web Microanalysis of Big Image Data
Author: Peter Bajcsy,Joe Chalfoun,Mylene Simon
Publsiher: Springer
Total Pages: 197
Release: 2018-01-22
ISBN: 3319633600
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Web Microanalysis of Big Image Data Book Excerpt:

This book looks at the increasing interest in running microscopy processing algorithms on big image data by presenting the theoretical and architectural underpinnings of a web image processing pipeline (WIPP). Software-based methods and infrastructure components for processing big data microscopy experiments are presented to demonstrate how information processing of repetitive, laborious and tedious analysis can be automated with a user-friendly system. Interactions of web system components and their impact on computational scalability, provenance information gathering, interactive display, and computing are explained in a top-down presentation of technical details. Web Microanalysis of Big Image Data includes descriptions of WIPP functionalities, use cases, and components of the web software system (web server and client architecture, algorithms, and hardware-software dependencies). The book comes with test image collections and a web software system to increase the reader's understanding and to provide practical tools for conducting big image experiments. By providing educational materials and software tools at the intersection of microscopy image analyses and computational science, graduate students, postdoctoral students, and scientists will benefit from the practical experiences, as well as theoretical insights. Furthermore, the book provides software and test data, empowering students and scientists with tools to make discoveries with higher statistical significance. Once they become familiar with the web image processing components, they can extend and re-purpose the existing software to new types of analyses. Each chapter follows a top-down presentation, starting with a short introduction and a classification of related methods. Next, a description of the specific method used in accompanying software is presented. For several topics, examples of how the specific method is applied to a dataset (parameters, RAM requirements, CPU efficiency) are shown. Some tips are provided as practical suggestions to improve accuracy or computational performance.

Mathematical Software ICMS 2014

Mathematical Software    ICMS 2014
Author: Hoon Hong,Chee Yap
Publsiher: Springer
Total Pages: 735
Release: 2014-08-01
ISBN: 3662441993
Category: Computers
Language: EN, FR, DE, ES & NL

Mathematical Software ICMS 2014 Book Excerpt:

This book constitutes the proceedings of the 4th International Conference on Mathematical Software, ICMS 2014, held in Seoul, South Korea, in August 2014. The 108 papers included in this volume were carefully reviewed and selected from 150 submissions. The papers are organized in topical sections named: invited; exploration; group; coding; topology; algebraic; geometry; surfaces; reasoning; special; Groebner; triangular; parametric; interfaces and general.

Mastering Java for Data Science

Mastering Java for Data Science
Author: Alexey Grigorev
Publsiher: Packt Publishing Ltd
Total Pages: 364
Release: 2017-04-27
ISBN: 1785887394
Category: Computers
Language: EN, FR, DE, ES & NL

Mastering Java for Data Science Book Excerpt:

Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.

Java Data Science Made Easy

Java  Data Science Made Easy
Author: Richard M. Reese,Jennifer L. Reese,Alexey Grigorev
Publsiher: Packt Publishing Ltd
Total Pages: 715
Release: 2017-07-07
ISBN: 1788479181
Category: Computers
Language: EN, FR, DE, ES & NL

Java Data Science Made Easy Book Excerpt:

Data collection, processing, analysis, and more About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples A highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks. Who This Book Is For This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you! What You Will Learn Understand the key concepts of data science Explore the data science ecosystem available in Java Work with the Java APIs and techniques used to perform efficient data analysis Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images, and create your own search Learn how to build deep neural networks with DeepLearning4j Build data science applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: Java for Data Science Mastering Java for Data Science Style and approach This course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Practical Data Science with Hadoop and Spark

Practical Data Science with Hadoop and Spark
Author: Ofer Mendelevitch,Casey Stella,Douglas Eadline
Publsiher: Addison-Wesley Professional
Total Pages: 256
Release: 2016-12-08
ISBN: 0134029720
Category: Computers
Language: EN, FR, DE, ES & NL

Practical Data Science with Hadoop and Spark Book Excerpt:

The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language

Scientific Computing with Scala

Scientific Computing with Scala
Author: Vytautas Jancauskas
Publsiher: Packt Publishing Ltd
Total Pages: 232
Release: 2016-04-27
ISBN: 1785887475
Category: Computers
Language: EN, FR, DE, ES & NL

Scientific Computing with Scala Book Excerpt:

Learn to solve scientific computing problems using Scala and its numerical computing, data processing, concurrency, and plotting libraries About This Book Parallelize your numerical computing code using convenient and safe techniques. Accomplish common high-performance, scientific computing goals in Scala. Learn about data visualization and how to create high-quality scientific plots in Scala Who This Book Is For Scientists and engineers who would like to use Scala for their scientific and numerical computing needs. A basic familiarity with undergraduate level mathematics and statistics is expected but not strictly required. A basic knowledge of Scala is required as well as the ability to write simple Scala programs. However, complicated programming concepts are not used in the book. Anyone who wants to explore using Scala for writing scientific or engineering software will benefit from the book. What You Will Learn Write and read a variety of popular file formats used to store scientific data Use Breeze for linear algebra, optimization, and digital signal processing Gain insight into Saddle for data analysis Use ScalaLab for interactive computing Quickly and conveniently write safe parallel applications using Scala's parallel collections Implement and deploy concurrent programs using the Akka framework Use the Wisp plotting library to produce scientific plots Visualize multivariate data using various visualization techniques In Detail Scala is a statically typed, Java Virtual Machine (JVM)-based language with strong support for functional programming. There exist libraries for Scala that cover a range of common scientific computing tasks – from linear algebra and numerical algorithms to convenient and safe parallelization to powerful plotting facilities. Learning to use these to perform common scientific tasks will allow you to write programs that are both fast and easy to write and maintain. We will start by discussing the advantages of using Scala over other scientific computing platforms. You will discover Scala packages that provide the functionality you have come to expect when writing scientific software. We will explore using Scala's Breeze library for linear algebra, optimization, and signal processing. We will then proceed to the Saddle library for data analysis. If you have experience in R or with Python's popular pandas library you will learn how to translate those skills to Saddle. If you are new to data analysis, you will learn basic concepts of Saddle as well. Well will explore the numerical computing environment called ScalaLab. It comes bundled with a lot of scientific software readily available. We will use it for interactive computing, data analysis, and visualization. In the following chapters, we will explore using Scala's powerful parallel collections for safe and convenient parallel programming. Topics such as the Akka concurrency framework will be covered. Finally, you will learn about multivariate data visualization and how to produce professional-looking plots in Scala easily. After reading the book, you should have more than enough information on how to start using Scala as your scientific computing platform Style and approach Examples are provided on how to use Scala to do basic numerical and scientific computing tasks. All the concepts are illustrated with more involved examples in each chapter. The goal of the book is to allow you to translate existing experience in scientific computing to Scala.