Machine Learning and Cognition in Enterprises, Rohit Kumar
Автор: Petra Perner Название: Machine Learning and Data Mining in Pattern Recognition ISBN: 3319210238 ISBN-13(EAN): 9783319210230 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
Автор: Alexander Gelbukh; F?lix Castro Espinoza; Sof?a N. Название: Nature-Inspired Computation and Machine Learning ISBN: 3319136496 ISBN-13(EAN): 9783319136493 Издательство: Springer Рейтинг: Цена: 10062.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It contains 44 papers structured into seven sections: natural language processing, natural language processing applications, opinion mining, sentiment analysis, and social network applications, computer vision, image processing, logic, reasoning, and multi-agent systems, and intelligent tutoring systems.
Автор: Han Liu; Mihaela Cocea Название: Granular Computing Based Machine Learning ISBN: 331970057X ISBN-13(EAN): 9783319700571 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction, - Traditional Machine Learning, - Semi-supervised Learning through Machine Based Labelling, - Nature Inspired Semi-heuristic Learning, - Fuzzy Classification through Generative Multi-task Learning, - Multi-granularity Semi-random Data Partitioning, - Multi-granularity Rule Learning, - Case Studies, - Con
Автор: Manas A. Pathak Название: Privacy-Preserving Machine Learning for Speech Processing ISBN: 1489991204 ISBN-13(EAN): 9781489991201 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification and speech recognition.
Автор: Valeri Mladenov; Petia Koprinkova-Hristova; G?nthe Название: Artificial Neural Networks and Machine Learning -- ICANN 2013 ISBN: 3642407277 ISBN-13(EAN): 9783642407277 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.
Автор: Luc de Raedt; Peter Flach Название: Machine Learning: ECML 2001 ISBN: 3540425365 ISBN-13(EAN): 9783540425366 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text constitutes the proceedings of the 12th European Conference on Machine Learning, 2001 and presents 50 revised papers and four invited contributions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, Web mining and inductive logic programming.
Автор: Achim Zielesny Название: From Curve Fitting to Machine Learning ISBN: 3319325442 ISBN-13(EAN): 9783319325446 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Experimental data analysis is at the core of scientific inquiry, and computers have taken this function to a new level. This volume is an interactive guide to complex modern analytical processes from non-linear curve fitting to clustering and machine learning.
Автор: Valentine Fontama; Roger Barga; Wee Hyong Tok Название: Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition ISBN: 1484212010 ISBN-13(EAN): 9781484212011 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.
Автор: Stefan Wermter; Cornelius Weber; Wlodzislaw Duch; Название: Artificial Neural Networks and Machine Learning -- ICANN 2014 ISBN: 3319111787 ISBN-13(EAN): 9783319111780 Издательство: Springer Рейтинг: Цена: 13416.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks;
Автор: Aboul Ella Hassanien; Abdel-Badeeh M. Salem; Rabie Название: Advanced Machine Learning Technologies and Applications ISBN: 3642353258 ISBN-13(EAN): 9783642353253 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2012, held in Cairo, Egypt, in December 2012.
Автор: Dipanjan Sarkar; Raghav Bali; Tushar Sharma Название: Practical Machine Learning with Python ISBN: 1484232062 ISBN-13(EAN): 9781484232064 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.
Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.
Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.
Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.
Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today
What You'll Learn
Execute end-to-end machine learning projects and systems
Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
Apply a wide range of machine learning models including regression, classification, and clustering.
Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.
Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
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