Scaling up machine learning, 

Автор: Strang Gilbert
Название: Linear Algebra and Learning from Data
ISBN: 0692196382 ISBN-13(EAN): 9780692196380
Издательство: Cambridge Academ
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Цена: 9978.00 р.
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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: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 11878.00 р.
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Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
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Цена: 18622.00 р.
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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.

Автор: Darren Cook
Название: Practical Machine Learning with H2O
ISBN: 149196460X ISBN-13(EAN): 9781491964606
Издательство: Wiley
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Цена: 6334.00 р.
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Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

Автор: Alpaydin Ethem
Название: Machine Learning: The New AI
ISBN: 0262529513 ISBN-13(EAN): 9780262529518
Издательство: MIT Press
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Цена: 2700.00 р.
Наличие на складе: Нет в наличии.

Описание:

A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.

Автор: Bekkerman
Название: Scaling up Machine Learning
ISBN: 0521192242 ISBN-13(EAN): 9780521192248
Издательство: Cambridge Academ
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Цена: 14731.00 р.
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Описание: In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.

Автор: I. Borg; P. J. F. Groenen
Название: Modern Multidimensional Scaling
ISBN: 1441920463 ISBN-13(EAN): 9781441920461
Издательство: Springer
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Цена: 24456.00 р.
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Описание: The first edition was released in 1996 and has sold close to 2200 copies. Provides an up-to-date comprehensive treatment of MDS, a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. The authors have added three chapters and exercise sets.

Автор: Conway Drew, White John Myles
Название: Machine Learning for Hackers
ISBN: 1449303714 ISBN-13(EAN): 9781449303716
Издательство: Wiley
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Цена: 6334.00 р.
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Описание: Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data.

Название: Learning machine translation
ISBN: 0262072971 ISBN-13(EAN): 9780262072977
Издательство: MIT Press
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Цена: 1709.00 р.
Наличие на складе: Нет в наличии.

Описание: The Internet gives us access to a wealth of information in languages we don`t understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This title investigates how Machine Learning techniques can improve Statistical Machine Translation.

Автор: Koehn, Philipp
Название: Statistical machine translation
ISBN: 0521874157 ISBN-13(EAN): 9780521874151
Издательство: Cambridge Academ
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Цена: 10454.00 р.
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Описание: Automatic language translation systems like those used by Google, have been revolutionized by recent advances in the methods used in statistical machine translation. This first textbook on the topic explains these innovations carefully and shows the reader, whether a student or a developer, how to build their own translation system.

Автор: Boyd Stephen
Название: Introduction to Applied Linear Algebra
ISBN: 1316518965 ISBN-13(EAN): 9781316518960
Издательство: Cambridge Academ
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Цена: 6811.00 р.
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Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.

Автор: Samantha Kleinberg
Название: Time and Causality Across the Sciences
ISBN: 1108476678 ISBN-13(EAN): 9781108476676
Издательство: Cambridge Academ
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Цена: 9186.00 р.
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Описание: This book provides an entry point for researchers in any field, bringing together perspectives collected from a large body of work on causality across disciplines. Topics include whether quantum mechanics allows causes to precede their effects, the integration of mechanisms, and insight into the role played by intervention and timing information.