A Hands-On Introduction to Data Science, Chirag Shah
Автор: Jeffrey Hoffstein and Jill Pipher Название: Introduction to Mathematical Cryptography ISBN: 1493917102 ISBN-13(EAN): 9781493917105 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Mathematical Cryptography
Автор: Sutton Richard S., Barto Andrew G. Название: Reinforcement Learning: An Introduction, 2 ed. ISBN: 0262039249 ISBN-13(EAN): 9780262039246 Издательство: MIT Press Рейтинг: Цена: 18850.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core, online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new for the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Автор: Saltz Jeffrey S., Stanton Jeffrey M. Название: An Introduction to Data Science ISBN: 150637753X ISBN-13(EAN): 9781506377537 Издательство: Sage Publications Рейтинг: Цена: 13306.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout.
Автор: M. Antonia Amaral Turkman, Carlos Daniel Paulino, Peter Muller Название: Computational Bayesian Statistics: An Introduction ISBN: 1108481035 ISBN-13(EAN): 9781108481038 Издательство: Cambridge Academ Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user`s guide for researchers and graduate students from beyond statistics.
Автор: S. Sumathi; S.N. Sivanandam Название: Introduction to Data Mining and its Applications ISBN: 3662500809 ISBN-13(EAN): 9783662500804 Издательство: Springer Рейтинг: Цена: 41787.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining.
Автор: Healy Kieran Название: Data Visualization: A Practical Introduction ISBN: 0691181624 ISBN-13(EAN): 9780691181622 Издательство: Wiley Рейтинг: Цена: 6653.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An accessible primer on how to create effective graphics from data
This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way.
Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible.
Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings.
Provides hands-on instruction using R and ggplot2
Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistent
Includes a library of data sets, code, and functions
Автор: Yang, Xin-She Название: Yang - INTRODUCTION TO ALGORITHMS FOR DATA MINING AND MAC... ISBN: 0128172169 ISBN-13(EAN): 9780128172162 Издательство: Elsevier Science Рейтинг: Цена: 9936.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning as well as optimization. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modelling skills so they can process and interpret data for classification, clustering, curve-fitting, and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
Автор: Agresti, Alan, Название: An Introduction to Categorical Data Analysis, 3rd Edition ISBN: 1119405262 ISBN-13(EAN): 9781119405269 Издательство: Wiley Рейтинг: Цена: 19317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A valuable new edition of a standard reference
The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data.
Adding to the value in the new edition is:
- Illustrations of the use of R software to perform all the analyses in the book
- A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis
- New sections in many chapters introducing the Bayesian approach for the methods of that chapter
- More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets
- An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises
Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Автор: Miroslav Kubat Название: An Introduction to Machine Learning ISBN: 3319876694 ISBN-13(EAN): 9783319876696 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Поставка под заказ.
Описание: This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications.
Автор: Kimbrough Название: Introduction To Business Analytics ISBN: 1482221764 ISBN-13(EAN): 9781482221763 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making.
Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel(R), GAMS, MATLAB(R), and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.
Автор: Igual, Laura, Segu?, Santi Название: Introduction to data science. ISBN: 3319500163 ISBN-13(EAN): 9783319500164 Издательство: Springer Рейтинг: Цена: 6841.00 р. Наличие на складе: Поставка под заказ.
Описание: The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis.
Автор: Graesser Laura Harding, Wah Loon Keng Название: Deep Reinforcement Learning in Python: A Hands-On Introduction ISBN: 0135172381 ISBN-13(EAN): 9780135172384 Издательство: Pearson Education Рейтинг: Цена: 7522.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
Understand each key aspect of a deep RL problem
Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
Understand how algorithms can be parallelized synchronously and asynchronously
Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
Explore algorithm benchmark results with tuned hyperparameters
Understand how deep RL environments are designed
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
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