Python for probability, statistics, and machine learning, Unpingco, Jose
Автор: Gauch, Jr Название: Scientific Method in Brief ISBN: 1107666724 ISBN-13(EAN): 9781107666726 Издательство: Cambridge Academ Рейтинг: Цена: 43290.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Specifically designed to enhance perspective and encourage productivity, this is a guide to the key principles of scientific method including deductive and inductive logic, probability, parsimony and hypothesis testing. The examples and case studies span the physical, biological and social sciences and also highlight science`s interrelationship with the humanities.
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: Michael C. Whitlock, Dolph Schluter Название: The Analysis of Biological Data ISBN: 1319325343 ISBN-13(EAN): 9781319325343 Издательство: Macmillan Learning Рейтинг: Цена: 92390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The evolution of a classicThe new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.
Автор: Unpingco Jose Название: Python for Probability, Statistics, and Machine Learning ISBN: 3030185443 ISBN-13(EAN): 9783030185442 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Невозможна поставка. Описание: ¢ Are you member of either the management team or the board and do you see the need to adapt your organization to Agile? Do you intend to make the organization more agile? ¢ Are you a coach helping organizations in the transformation to becoming more agile? And are you planning to support this transition using a bottom-up or top-down approach?In this pocket guide you will find a practical approach on how to handle this. Governing an organization in a fast-changing world. And all this although the issues of the day require a lot of your attention and can distract you from the results you want to achieve. The authors consider how to operationalize the organization`s strategic goals and consequently the governance of the entire organization.The authors start from the position of: ¢ Clarifying what has to be achieved in the next quarter in order to achieve the strategic goals. ¢ Introducing a system of short cyclical adjustments, with which you can respond to changing demand from customers or emerging laws and regulations. ¢ Working closely together as management team or board towards the long-term strategic goals and preventing everyone within the organization from following their own goals. ¢ Bringing more focus on the operationalization of the strategy, less `fire-fighting` and greater emphasis on fire prevention. ¢ Getting a clear picture of what prevents your employees from doing their jobs effectively. Will you succeed in removing the barriers holding back your organization? The core message of this pocket guide is application of the FOCUS board. This is a visual approach to management and a strong tool for governing the organization. When this is applied, it will result in collaboration between all layers of the organization, enable short cyclical adjustment and provide a clear focus on achieving the strategic goals.
Автор: Unpingco Josй Название: Python for Probability, Statistics, and Machine Learning ISBN: 3030185478 ISBN-13(EAN): 9783030185473 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas.
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 73920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Автор: Samantha Kleinberg Название: Time and Causality Across the Sciences ISBN: 1108476678 ISBN-13(EAN): 9781108476676 Издательство: Cambridge Academ Рейтинг: Цена: 63360.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Paul H.C. Eilers, Brian D. Marx Название: Practical Smoothing: The Joys of P-splines ISBN: 1108482953 ISBN-13(EAN): 9781108482950 Издательство: Cambridge Academ Рейтинг: Цена: 57030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: P-splines are widely used in statistics and machine learning for smoothing out noise in data and to avoid overtraining. This practical guide covers theory and a range of standard and non-standard applications with code in R for professionals and researchers looking for a simple, flexible and powerful smoothing tool.
Автор: Darwiche Название: Modeling and Reasoning with Bayesian Networks ISBN: 1107678420 ISBN-13(EAN): 9781107678422 Издательство: Cambridge Academ Рейтинг: Цена: 65470.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.
Автор: Donald B. Percival, Andrew T. Walden Название: Spectral Analysis for Univariate Time Series ISBN: 1107028140 ISBN-13(EAN): 9781107028142 Издательство: Cambridge Academ Рейтинг: Цена: 97150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.
Автор: Morrison, Faith A. Название: Uncertainty analysis for engineers and scientists ISBN: 1108745741 ISBN-13(EAN): 9781108745741 Издательство: Cambridge Academ Рейтинг: Цена: 46470.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Whether you are new to the sciences or an experienced engineer, this useful text provides a practical approach to performing error analysis.
Автор: Anders Hald Название: A History of Probability and Statistics and Their Applications before 1750 ISBN: 0471471291 ISBN-13(EAN): 9780471471295 Издательство: Wiley Рейтинг: Цена: 152010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistics have helped shape every area of science. Without the means to analyze critical data, none of the great disoveries of the past would be possible. This paperback reprint of a Wiley bestseller shows the development of these data analysis tools and the manner in which they aided technological development prior to 1750.
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