Описание: Written by HPC experts, this book provides a solid introduction to current mainstream computer architecture, dominant parallel programming models, and useful optimization strategies for scientific HPC. It facilitates an intuitive understanding of performance limitations without relying on heavy computer science knowledge.
Автор: Foucart Simon Название: Mathematical Introduction to Compressive Sensing ISBN: 0817649476 ISBN-13(EAN): 9780817649470 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.
Описание: Now inits second edition, this textbook serves as an introduction toprobability and statistics for non-mathematics majors who do not need theexhaustive detail and mathematical depth provided in more comprehensivetreatments of the subject. The presentation covers the mathematical laws ofrandom phenomena, including discrete and continuous random variables,expectation and variance, and common probability distributions such as thebinomial, Poisson, and normal distributions. More classical examples such asMontmort's problem, the ballot problem, and Bertrand’s paradox are nowincluded, along with applications such as the Maxwell-Boltzmann andBose-Einstein distributions in physics.Keyfeatures in new edition:* 35 newexercises* Expanded sectionon the algebra of sets *Expanded chapters on probabilities to include more classical examples* Newsection on regression* Onlineinstructors' manual containing solutions to all exercises
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: Taweh Beysolow II Название: Introduction to Deep Learning Using R ISBN: 1484227336 ISBN-13(EAN): 9781484227336 Издательство: Springer Рейтинг: Цена: 5309.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Understand deep learning, the nuances of its different models, and where these models can be applied.
The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You Will Learn:
Understand the intuition and mathematics that power deep learning models
Utilize various algorithms using the R programming language and its packages
Use best practices for experimental design and variable selection
Practice the methodology to approach and effectively solve problems as a data scientist
Evaluate the effectiveness of algorithmic solutions and enhance their predictive power
Who this book is for: Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
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