Data Management in Machine Learning Systems, Boehm Matthias, Kumar Arun, Yang Jun
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 66520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T Наличие на складе: Невозможна поставка. Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Автор: Steven L. Brunton, J. Nathan Kutz Название: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control ISBN: 1108422098 ISBN-13(EAN): 9781108422093 Издательство: Amazon Internet Рейтинг: Цена: 0.00 T Наличие на складе: Невозможна поставка. Описание: Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. Aimed at advanced undergraduate and beginning graduate students, this textbook provides an integrated viewpoint that shows how to apply emerging methods from data science, data mining, and machine learning to engineering and the physical sciences.
Автор: Grolemund Garrett, Wickham Hadley Название: R for Data Science ISBN: 1491910399 ISBN-13(EAN): 9781491910399 Издательство: Wiley Рейтинг: Цена: 46450.00 T Наличие на складе: Невозможна поставка. Описание: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Название: High Performance Programming for Soft Computing ISBN: 146658601X ISBN-13(EAN): 9781466586017 Издательство: Taylor&Francis Рейтинг: Цена: 153120.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book examines the present and future of soft computer techniques. It explains how to use the latest technological tools, such as multicore processors and graphics processing units, to implement highly efficient intelligent system methods using a general purpose computer.
Автор: Witten, Ian H. Название: Data Mining. Practical Machine Learning Tools and Techniques, 4 ed. ISBN: 0128042915 ISBN-13(EAN): 9780128042915 Издательство: Elsevier Science Рейтинг: Цена: 61750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Miroslav Kubat Название: An Introduction to Machine Learning ISBN: 3319348868 ISBN-13(EAN): 9783319348865 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
Название: Deep learning in biometrics ISBN: 1138578231 ISBN-13(EAN): 9781138578234 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book will cover all the topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoenders. The focus will be on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints.
Автор: Matthias Boehm, Arun Kumar, Jun Yang Название: Data Management in Machine Learning Systems ISBN: 1681734982 ISBN-13(EAN): 9781681734989 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 87780.00 T Наличие на складе: Нет в наличии. Описание: Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators; data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.
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