Описание: Racism and white supremacy have survived in the United States for a very long time because from the onset, it was based on sound `engineering.` The book contends that racism is not a belief, practice, or ideology that flared up by accident or through the spur of the moment. Rather, it followed sound engineering stages like planning, design, and construction. Racism was designed to serve a domineering purpose for white people and, therefore, there was a lot of planning that eventually led to its design and construction. Because the architects had a vision of its permanency, they chose the most durable materials for its construction. Metaphorically, they used mortar, bricks, and steel beams. These durable materials, among others, include religion, science, government (including the Supreme Court), the constitution and laws, brutality, and social media. As the centuries rolled by, succeeding architects of this design and construction have done a superb job in maintenance and modifications to elude stumbling blocks. Based on the times and prevailing winds, the racism construct has undergone mutations to evade capture and destruction - even to this day. Like a car, the engineering and engineers have evolved, the models and make are changing, but the underlying engineering remains intact. Today, the overt, brash, and brutal racism has generally ceded to an equally destructive, calculated, politically-correct, less pompous, highly sophisticated, and veiled racism. The book dissects this durable foundational construct and proffers recommendations that will systematically minimize its intensity.
Описание: The increased and widespread availability of large network data resources in recent years has resulted in the increased need for effective methods for the analysis of these networks. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straight forward task because of the size of the data sets and the computer power required for the analyses. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this inter-disciplinary text provides an ideal introduction to and insight into the field of network data analysis.
Автор: John M. Stewart Название: Python for Scientists ISBN: 1316641236 ISBN-13(EAN): 9781316641231 Издательство: Cambridge Academ Рейтинг: Цена: 5067.00 р. Наличие на складе: Поставка под заказ.
Описание: Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.
Описание: Bringing together leading experts in the field of network data analysis, this text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning. Using real-world biological and medical examples, applications of these theories are discussed and creative thinking is encouraged in the analysis of such complex network data sets.
Автор: Matt A. Wood Название: Python and Matplotlib Essentials for Scientists and Engineers ISBN: 1681749106 ISBN-13(EAN): 9781681749105 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 6237.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download.Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
Автор: Juana Sanchez Название: Probability for Data Scientists ISBN: 1516532694 ISBN-13(EAN): 9781516532698 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15042.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.
Автор: G?nter Mayer Название: Interval Analysis: and Automatic Result Verification ISBN: 3110500639 ISBN-13(EAN): 9783110500639 Издательство: Walter de Gruyter Рейтинг: Цена: 22305.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This self-contained text is a step-by-step introduction and a complete overview of interval computation and result verification, a subject whose importance has steadily increased over the past many years. The author, an expert in the field, gently presents the theory of interval analysis through many examples and exercises, and guides the reader from the basics of the theory to current research topics in the mathematics of computation.
Contents
Preliminaries
Real intervals
Interval vectors, interval matrices
Expressions, P-contraction, ε-inflation
Linear systems of equations
Nonlinear systems of equations
Eigenvalue problems
Automatic differentiation
Complex intervals
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