Scaling Up Machine Learning

Parallel and Distributed Approaches

Author: Ron Bekkerman,Mikhail Bilenko,John Langford

Publisher: Cambridge University Press

ISBN: 0521192242

Category: Computers

Page: 475

View: 6565

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Machine Learning for Adaptive Many-Core Machines - A Practical Approach

Author: Noel Lopes,Bernardete Ribeiro

Publisher: Springer

ISBN: 3319069381

Category: Computers

Page: 241

View: 7684

The overwhelming data produced everyday and the increasing performance and cost requirements of applications are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.

Large Scale Machine Learning with Spark

Author: Md. Rezaul Karim,Md. Mahedi Kaysar

Publisher: Packt Publishing Ltd

ISBN: 1785883712

Category: Computers

Page: 476

View: 6430

Discover everything you need to build robust machine learning applications with Spark 2.0 About This Book Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2.0.0 Use Spark's machine learning library in a big data environment You will learn how to develop high-value applications at scale with ease and a develop a personalized design Who This Book Is For This book is for data science engineers and scientists who work with large and complex data sets. You should be familiar with the basics of machine learning concepts, statistics, and computational mathematics. Knowledge of Scala and Java is advisable. What You Will Learn Get solid theoretical understandings of ML algorithms Configure Spark on cluster and cloud infrastructure to develop applications using Scala, Java, Python, and R Scale up ML applications on large cluster or cloud infrastructures Use Spark ML and MLlib to develop ML pipelines with recommendation system, classification, regression, clustering, sentiment analysis, and dimensionality reduction Handle large texts for developing ML applications with strong focus on feature engineering Use Spark Streaming to develop ML applications for real-time streaming Tune ML models with cross-validation, hyperparameters tuning and train split Enhance ML models to make them adaptable for new data in dynamic and incremental environments In Detail Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application. Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce. This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications. This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you'll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark. In summary, you will be able to develop complete and personalised ML applications from data collections,model building, tuning, and scaling up to deploying on a cluster or the cloud. Style and approach This book takes a practical approach where all the topics explained are demonstrated with the help of real-world use cases.

Machine Learning Models and Algorithms for Big Data Classification

Thinking with Examples for Effective Learning

Author: Shan Suthaharan

Publisher: Springer

ISBN: 1489976418

Category: Business & Economics

Page: 359

View: 5248

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

Computational Learning Theory and Natural Learning Systems: Making learning systems practical

Author: Russell Greiner,Stephen José Hanson,Thomas Petsche

Publisher: MIT Press

ISBN: 9780262571180

Category: Computers

Page: 407

View: 6610

This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors: Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador

Large Scale Machine Learning with Python

Author: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

Publisher: Packt Publishing Ltd

ISBN: 1785888021

Category: Computers

Page: 420

View: 519

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

Neuronale Netze selbst programmieren

Ein verständlicher Einstieg mit Python

Author: Tariq Rashid

Publisher: O'Reilly

ISBN: 3960101031

Category: Computers

Page: 232

View: 8369

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Data mining

praktische Werkzeuge und Techniken für das maschinelle Lernen

Author: Ian H. Witten,Eibe Frank

Publisher: N.A

ISBN: 9783446215337

Category:

Page: 386

View: 572

Machine Learning Methods for Planning

Author: Steven Minton

Publisher: Morgan Kaufmann Publishers

ISBN: 9781558602489

Category: Business & Economics

Page: 540

View: 4011

Captain Call, now a bounty hunter hired to catch bandit Joey Garza, assembles a group of unlikely assistants and travels to Crowtown Texas.

Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning

Adaptation and Multi-Agent Learning, 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems, Revised Selected Papers

Author: Karl Tuyls,Ann Nowe,Zahia Guessoum,Daniel Kudenko

Publisher: Springer Science & Business Media

ISBN: 3540779477

Category: Computers

Page: 258

View: 7924

This book contains selected and revised papers of the European Symposium on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS), editions 2005, 2006 and 2007, held in Paris, Brussels and Maastricht. The goal of the ALAMAS symposia, and this associated book, is to increase awareness and interest in adaptation and learning for single agents and mul- agent systems, and encourage collaboration between machine learning experts, softwareengineeringexperts,mathematicians,biologistsandphysicists,andgive a representative overviewof current state of a?airs in this area. It is an inclusive forum where researchers can present recent work and discuss their newest ideas for a ?rst time with their peers. Thesymposiaseriesfocusesonallaspectsofadaptiveandlearningagentsand multi-agent systems, with a particular emphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems. These symposia were a great success and provided a forum for the pres- tation of new ideas and results bearing on the conception of adaptation and learning for single agents and multi-agent systems. Over these three editions we received 51 submissions, of which 17 were carefully selected, including one invited paper of this year’s invited speaker Simon Parsons. This is a very c- petitive acceptance rate of approximately 31%, which, together with two review cycles, has led to a high-quality LNAI volume. We hope that our readers will be inspired by the papers included in this volume.

Towards Intelligent Engineering and Information Technology

Author: Imre J. Rudas,János Fodor,Janusz Kacprzyk

Publisher: Springer Science & Business Media

ISBN: 3642037364

Category: Computers

Page: 736

View: 512

This book presents the state of the art of computational intelligence ion engineering. It offers challenging problems for efficient modeling of intelligent systems and details different methodologies of computational intelligence with real life applications.

Das Geheimnis des menschlichen Denkens

Einblicke in das Reverse Engineering des Gehirns

Author: Ray Kurzweil

Publisher: Lola Books

ISBN: 394420316X

Category: Science

Page: 352

View: 4395

Der Wettlauf um das Gehirn hat begonnen. Sowohl die EU als auch die USA haben gewaltige Forschungsprojekte ins Leben gerufen um das Geheimnis des menschlichen Denkens zu entschlüsseln. 2023 soll es dann soweit sein: Das menschliche Gehirn kann vollständig simuliert werden. In "Das Geheimnis des menschlichen Denkens" gewährt Googles Chefingenieur Ray Kurzweil einen spannenden Einblick in das Reverse Engineering des Gehirns. Er legt dar, wie mithilfe der Mustererkennungstheorie des Geistes der ungeheuren Komplexität des Gehirns beizukommen ist und wirft einen ebenso präzisen wie überraschenden Blick auf die am Horizont sich bereits abzeichnende Zukunft. Ist das menschliche Gehirn erst einmal simuliert, wird künstliche Intelligenz die Fähigkeiten des Menschen schon bald übertreffen. Ein Ereignis, das Kurzweil aufgrund der bereits in "Menschheit 2.0" entworfenen exponentiellen Wachstumskurve der Informationstechnologien bereits für das Jahr 2029 prognostiziert. Aber was dann? Kurzweil ist zuversichtlich, dass die Vorteile künstlicher Intelligenz mögliche Bedrohungsszenarien überwiegen und sie uns entscheidend dabei hilft, uns weiterzuentwickeln und die Herausforderungen der Zukunft zu meistern.

Das Ende der Arbeit und ihre Zukunft

Neue Konzepte für das 21. Jahrhundert

Author: Jeremy Rifkin

Publisher: Campus Verlag

ISBN: 3593400855

Category: Political Science

Page: 240

View: 2931

Die Arbeit hat sich im letzten Jahrzehnt weiter verändert. Bereits in 50 Jahren werden weniger als 10 Prozent der Bevölkerung ausreichen, um alle Güter und Dienstleistungen bereitzustellen. Die Konsequenzen für die sozialen Sicherungssysteme sind dramatisch, soziale Konlikte scheinen unvermeidlich. Dass "es nicht mehr genug Arbeit für alle geben wird" erkannte Jeremy Rifkin bereits in seinem Weltbesteller Das Ende der Arbeit - und seine Thesen sind heute aktueller denn je. In der Neuausgabe des in 16 Sprachen übersetzten Bestsellers entwickelt Rifkin seine radikalen Vorschläge weiter und zeigt mit gewohntem wirtschaftlichen und politischen Sachverstand, wie wir verhindern können, dass uns die Arbeit ausgeht. "Rifkins Buch wird uns noch lange beschäftigen." Süddeutsche Zeitung

Data Algorithms

Recipes for Scaling Up with Hadoop and Spark

Author: Mahmoud Parsian

Publisher: "O'Reilly Media, Inc."

ISBN: 1491906154

Category: Computers

Page: 778

View: 3404

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

Large-Scale Machine Learning in the Earth Sciences

Author: Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser

Publisher: CRC Press

ISBN: 1315354462

Category: Computers

Page: 208

View: 6355

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Advances in Distributed and Parallel Knowledge Discovery

Author: Hillol Kargupta,Philip Chan

Publisher: Aaai Press

ISBN: 9780262611558

Category: Computers

Page: 467

View: 1085

foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.Contributors Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang.

Machine Learning: ECML-98

10th European Conference on Machine Learning, Chemnitz, Germany, April 21-23, 1998, Proceedings

Author: Claire Nedellec

Publisher: Springer Verlag

ISBN: N.A

Category: Computers

Page: 420

View: 7758

This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998. The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were selected from a total of 100 submissions. The book is divided in sections on applications of ML, Bayesian networks, feature selection, decision trees, support vector learning, multiple models for classification, inductive logic programming, relational learning, instance-based learning, clustering, genetic algorithms, reinforcement learning and neural networks.

Machine Learning: ECML 2004

15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings

Author: Jean-Francois Boulicaut,Floriana Esposito,Fosca Giannotti,Dino Pedreschi

Publisher: Springer

ISBN: N.A

Category: Machine learning

Page: 580

View: 746

This book constitutes the refereed proceedings of the 15th European Conference on Machine Learning, ECML 2004, held in Pisa, Italy, in September 2004, jointly with PKDD 2004. The 45 revised full papers and 6 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 280 papers submitted to ECML and 107 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.