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Bandit Algorithms, Tor Lattimore, Csaba Szepesvari


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Цена: 6970.00р.
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Наличие: Поставка под заказ.  Есть в наличии на складе поставщика.
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При оформлении заказа до: 2025-08-04
Ориентировочная дата поставки: Август-начало Сентября

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Автор: Tor Lattimore, Csaba Szepesvari
Название:  Bandit Algorithms
Перевод названия: Тор Латтимор, Чаба Сепешвари: Бандитские алгоритмы
ISBN: 9781108486828
Издательство: Cambridge Academ
Классификация:




ISBN-10: 1108486827
Обложка/Формат: Hardcover
Страницы: 450
Вес: 0.42 кг.
Дата издания: 31.07.2020
Серия: Economics/Business/Finance
Язык: English
Иллюстрации: Worked examples or exercises
Размер: 181 x 253 x 35
Читательская аудитория: Professional and scholarly
Ключевые слова: Optimization,Algorithms & data structures,Mathematical theory of computation,Machine learning,Microeconomics, COMPUTERS / Computer Vision & Pattern Recognition
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks.


Introduction to algorithms  3 ed.

Автор: Cormen, Thomas H., E
Название: Introduction to algorithms 3 ed.
ISBN: 0262033844 ISBN-13(EAN): 9780262033848
Издательство: MIT Press
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Цена: 27588.00 р.
Наличие на складе: Нет в наличии.

Описание: A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-base flow.

Network Science

Автор: Barab?si
Название: Network Science
ISBN: 1107076269 ISBN-13(EAN): 9781107076266
Издательство: Cambridge Academ
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Цена: 7762.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of disciplines from physics to the social sciences, is the only book needed for an introduction to network science. In modular format, with clear delineation between undergraduate and graduate material, its unique design is supported by extensive online resources.

Optimization using evolutionary algorithms and metaheuristics

Автор: Kaushik Kumar and J. Paulo Davim
Название: Optimization using evolutionary algorithms and metaheuristics
ISBN: 0367260441 ISBN-13(EAN): 9780367260446
Издательство: Taylor&Francis
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Цена: 25265.00 р.
Наличие на складе: Поставка под заказ.

Описание: This book covers developments and advances of algorithm based optimization techniques These techniques were only used for non-engineering problems. This book applies them to engineering problems.

Sequential and Parallel Algorithms and Data Structures

Название: Sequential and Parallel Algorithms and Data Structures
ISBN: 3030252086 ISBN-13(EAN): 9783030252083
Издательство: Springer
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Цена: 6288.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This undergraduate textbook is a concise introduction to the basic toolbox of structures that allow efficient organization and retrieval of data, key algorithms for problems on graphs, and generic techniques for modeling, understanding, and solving algorithmic problems.

Julia for Data Science

Автор: Voulgaris Zacharias
Название: Julia for Data Science
ISBN: 1634621301 ISBN-13(EAN): 9781634621304
Издательство: Gazelle Book Services
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Цена: 6200.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

Bandit Algorithms for Website Optimization

Автор: White John
Название: Bandit Algorithms for Website Optimization
ISBN: 1449341330 ISBN-13(EAN): 9781449341336
Издательство: Wiley
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Цена: 2533.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book shows you how to run experiments on your website using A/B testing - and then takes you a huge step further by introducing you to bandit algorithms for website optimization.

Bandit Algorithms in Information Retrieval

Автор: Dorota Glowacka
Название: Bandit Algorithms in Information Retrieval
ISBN: 1680835742 ISBN-13(EAN): 9781680835748
Издательство: Mare Nostrum (Eurospan)
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Цена: 12197.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Provides an overview of bandit algorithms inspired by various aspects of Information Retrieval (IR), such as click models, online ranker evaluation, personalization or the cold-start problem. Using a survey style, each chapter focuses on a specific IR problem and explains how it was addressed with various bandit approaches.

Hands-On Data Structures and Algorithms with Python 2 ed

Автор: Agarwal, Dr Basant, Baka, Benjamin
Название: Hands-On Data Structures and Algorithms with Python 2 ed
ISBN: 1788995570 ISBN-13(EAN): 9781788995573
Издательство: Неизвестно
Рейтинг:
Цена: 8091.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Data structures help us to organize and align the data in a very efficient way. This book will surely help you to learn important and essential data structures through Python implementation for better understanding of the concepts.

Geometry and Complexity Theory

Автор: Landsberg JM
Название: Geometry and Complexity Theory
ISBN: 1107199239 ISBN-13(EAN): 9781107199231
Издательство: Cambridge Academ
Рейтинг:
Цена: 9662.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: A comprehensive introduction to algebraic geometry and representation theory written by a leading expert in the field. For graduate students and researchers in computer science and mathematics, the book demonstrates state-of-the-art techniques to solve real world problems, focusing on P vs NP and the complexity of matrix multiplication.

Stochastic Simulation: Algorithms and Analysis

Автор: Asmussen
Название: Stochastic Simulation: Algorithms and Analysis
ISBN: 038730679X ISBN-13(EAN): 9780387306797
Издательство: Springer
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Цена: 6981.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods , as well as accompanying mathematical analysis of the convergence properties of the methods discussed . The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first  half of the book focusses on general methods, whereas the second half discusses model-specific algorithms. Given the wide range of  examples, exercises and applications students, practitioners and researchers in  probability, statistics, operations research, economics, finance, engineering  as well as biology and chemistry and physics will find the book of value.  Soren Asmussen is Professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is Thomas Ford Professor of  Engineering at Stanford University. 

Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems

Автор: Bubeck Sebastien, Nicolo Cesa-Bianchi
Название: Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems
ISBN: 1601986262 ISBN-13(EAN): 9781601986269
Издательство: Неизвестно
Рейтинг:
Цена: 12415.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: In this monograph, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it analyzes some of the most important variants and extensions, such as the contextual bandit model.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Автор: Kelleher John D., Macnamee Brian, D`Arcy Aoife
Название: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
ISBN: 0262029448 ISBN-13(EAN): 9780262029445
Издательство: MIT Press
Рейтинг:
Цена: 13543.00 р.
Наличие на складе: Нет в наличии.

Описание:

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.


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