Big Data Et Machine Learning 3e D
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Big Data Et Machine Learning 3e D
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Author : Pirmin Lemberger
language : fr
Publisher: Dunod
Release Date : 2019-08-14
Big Data Et Machine Learning 3e D written by Pirmin Lemberger and has been published by Dunod this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-14 with Computers categories.
Cet ouvrage s’adresse à tous ceux qui cherchent à tirer parti de l’énorme potentiel des « technologies Big Data », qu’ils soient data scientists, DSI, chefs de projets ou spécialistes métier. Le Big Data s’est imposé comme une innovation majeure pour toutes les entreprises qui cherchent à construire un avantage concurrentiel grâce à l’exploitation de leurs données clients, fournisseurs, produits, processus, machines, etc. Mais quelle solution technique choisir ? Quelles compétences métier développer au sein de la DSI ? Ce livre est un guide pour comprendre les enjeux d’un projet Big Data, en appréhender les concepts sous-jacents (en particulier le Machine Learning) et acquérir les compétences nécessaires à la mise en place d’un data lab. Il combine la présentation : • de notions théoriques (traitement statistique des données, calcul distribué...) ; • des outils les plus répandus (écosystème Hadoop, Storm...) ; • d’exemples d’applications ; • d’une organisation typique d’un projet de data science. Les ajouts de cette troisième édition concernent principalement la vision d’architecture d’entreprise, nécessaire pour intégrer les innovations du Big Data au sein des organisations, et le Deep Learning pour le NLP (Natural Language Processing, qui est l’un des domaines de l’intelligence artificielle qui a le plus progressé récemment).
Machine Learning And Big Data Analytics Paradigms Analysis Applications And Challenges
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2020-12-14
Machine Learning And Big Data Analytics Paradigms Analysis Applications And Challenges written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-14 with Computers categories.
This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
Machine Learning Models And Algorithms For Big Data Classification
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Author : Shan Suthaharan
language : en
Publisher: Springer
Release Date : 2015-10-20
Machine Learning Models And Algorithms For Big Data Classification written by Shan Suthaharan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-20 with Business & Economics categories.
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.
Statistical And Machine Learning Data Mining
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Author : Bruce Ratner
language : en
Publisher: CRC Press
Release Date : 2017-07-12
Statistical And Machine Learning Data Mining written by Bruce Ratner and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Computers categories.
Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
Introduction To Data Science And Machine Learning
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Author : Keshav Sud
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-03-25
Introduction To Data Science And Machine Learning written by Keshav Sud and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-25 with Computers categories.
Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.
Big Data Data Mining And Machine Learning
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Author : Jared Dean
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-07
Big Data Data Mining And Machine Learning written by Jared Dean and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-07 with Computers categories.
With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.
Machine Learning Optimization And Big Data
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Author : Panos M. Pardalos
language : en
Publisher: Springer
Release Date : 2016-12-25
Machine Learning Optimization And Big Data written by Panos M. Pardalos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-25 with Computers categories.
This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions. These proceedings contain papers in the fields of Machine Learning, Computational Optimization and DataScience presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Recent Advances In Big Data And Deep Learning
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Author : Luca Oneto
language : en
Publisher: Springer
Release Date : 2019-04-02
Recent Advances In Big Data And Deep Learning written by Luca Oneto and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-02 with Computers categories.
This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.
Big Data Machine Learning And Applications
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Author : Ripon Patgiri
language : en
Publisher: Springer Nature
Release Date : 2020-11-27
Big Data Machine Learning And Applications written by Ripon Patgiri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-27 with Computers categories.
This book constitutes refereed proceedings of the First International First International Conference on Big Data, Machine Learning, and Applications, BigDML 2019, held in Silchar, India, in December. The 6 full papers and 3 short papers were carefully reviewed and selected from 152 submissions. The papers present research on such topics as computing methodology; machine learning; artificial intelligence; information systems; security and privacy.
Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics
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Author : R. Sujatha
language : en
Publisher: CRC Press
Release Date : 2021-09-22
Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics written by R. Sujatha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Technology & Engineering categories.
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.