Introduction to Self-Driving Vehicle Technology, Sjafrie, Hanky
Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh Название: Introduction to statistical learning ISBN: 1071614177 ISBN-13(EAN): 9781071614174 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Автор: Timbers, Tiffany-anne (university Of British Colum Название: Data science ISBN: 0367524686 ISBN-13(EAN): 9780367524685 Издательство: Taylor&Francis Рейтинг: Цена: 50010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.
Автор: Wooldridge, Michael Название: Introduction to multiagent systems ISBN: 0470519460 ISBN-13(EAN): 9780470519462 Издательство: Wiley Рейтинг: Цена: 60140.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The eagerly anticipated updated resource on one of the most important areas of research and development: multi-agent systems Multi-agent systems allow many intelligent agents to interact with each other, and this field of study has advanced at a rapid pace since the publication of the first edition of this book, which was nearly a decade ago.
Автор: Leyton-brown, Kevin Shoham, Yoav Название: Essentials of game theory ISBN: 1598295934 ISBN-13(EAN): 9781598295931 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 0.00 T Наличие на складе: Невозможна поставка. Описание: When Volkswagen launched the Golf GTI, over thirty years ago, it could hardly have known the impact its compact and sporty model would have on the car-buying public. Through an uncertain birth to its class-topping iconic status of today, rarely does a new model of car make a whole new market segment for itself but the Golf was the original hot hatch, a car that others would copy but seldom equal.
Автор: Oliver Kramer Название: A Brief Introduction to Continuous Evolutionary Optimization ISBN: 3319034219 ISBN-13(EAN): 9783319034218 Издательство: Springer Рейтинг: Цена: 60940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. It presents heuristic extensions that allow optimization in constrained, multimodal and multi-objective solution spaces.
Автор: Corea Francesco Название: An Introduction to Data: Everything You Need to Know about Ai, Big Data and Data Science ISBN: 303004467X ISBN-13(EAN): 9783030044671 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.
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Автор: Perros, Harry G. Название: An Introduction to IoT Analytics ISBN: 0367686317 ISBN-13(EAN): 9780367686314 Издательство: Taylor&Francis Рейтинг: Цена: 48990.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers techniques that can be used to analyze data from IoT sensors and also addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so that one can learn how to apply these tools in practice with a good understanding of their inner workings.
Автор: L. Huang Название: A Concise Introduction to Mechanics of Rigid Bodies ISBN: 3319831941 ISBN-13(EAN): 9783319831947 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Нет в наличии. Описание: With simplified mathematical equations that illuminate the theoretical foundations of robotics and mechanics, this volume provides comprehensive coverage of statics and dynamics together, giving readers a more balanced understanding of their relationship.
Автор: Pardos-gotor, Jose M. Название: Screw theory in robotics ISBN: 1032107367 ISBN-13(EAN): 9781032107363 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Screw theory is an effective and efficient method used in robotics applications. This book demonstrates how to implement screw theory, explaining the key fundamentals and real-world applications using a practical and visual approach. An essential tool for those involved in the development of robotics implementations, the book uses case studies to analyze mechatronics. Screw theory offers a significant opportunity to interpret mechanics at a high level, facilitating contemporary geometric techniques in solving common robotics issues. Using these solutions results in an optimized performance in comparison to algebraic and numerical options. Demonstrating techniques such as six-dimensional (6D) vector notation and the Product of Exponentials (POE), the use of screw theory notation reduces the need for complex algebra, which results in simpler code, which is easier to write, comprehend, and debug. The book provides exercises and simulations to demonstrate this with new formulas and algorithms presented to aid the reader in accelerating their learning. By walking the user through the fundamentals of screw theory, and by providing a complete set of examples for the most common robot manipulator architecture, the book delivers an excellent foundation through which to comprehend screw theory developments. The visual approach of the book means it can be used as a self-learning tool for professionals alongside students. It will be of interest to those studying robotics, mechanics, mechanical engineering, and electrical engineering.
Автор: Marc Stanford, Stanford Название: Age of ai ISBN: 1705603297 ISBN-13(EAN): 9781705603291 Издательство: Неизвестно Рейтинг: Цена: 11440.00 T Наличие на складе: Нет в наличии.
Автор: Korstanje, Joos Название: Machine learning on geographical data using python ISBN: 1484282868 ISBN-13(EAN): 9781484282861 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn * Understand the fundamental concepts of working with geodata * Work with multiple geographical data types and file formats in Python * Create maps in Python * Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment
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