Автор: Foucart Simon Название: Mathematical Introduction to Compressive Sensing ISBN: 0817649476 ISBN-13(EAN): 9780817649470 Издательство: Springer Рейтинг: Цена: 65210.00 T 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.
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.
This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.
Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
Автор: Schay G. Название: Introduction to Probability with Statistical Applications ISBN: 3319306189 ISBN-13(EAN): 9783319306186 Издательство: Springer Рейтинг: Цена: 62410.00 T Наличие на складе: Поставка под заказ. 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: Hager Название: Introduction to High Performance Computing for Scientists and Engineers ISBN: 143981192X ISBN-13(EAN): 9781439811924 Издательство: Taylor&Francis Рейтинг: Цена: 80630.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Igual, Laura, Segu?, Santi Название: Introduction to data science. ISBN: 3319500163 ISBN-13(EAN): 9783319500164 Издательство: Springer Рейтинг: Цена: 45610.00 T 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: 35390.00 T 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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