Название: An Introduction to Metaheuristics for Optimization
Автор: Bastien Chopard, Marco Tomassini
Издательство: Springer
Год: 2018
Страниц: 226
Формат: PDF
Размер: 10 Mb
Язык: English

The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies.
This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.

Название: Learning from Imbalanced Data Sets
Автор: Alberto Fern?ndez
Издательство: Springer
Год: 2018
Страниц: 377
Формат: PDF, EPUB
Размер: 39 Mb
Язык: English

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge.

Название: Embedded System Design: Introduction to SoC System Architecture
Автор: Mohit Arora
Издательство: Learning Bytes Publishing
Год: 2016
ISBN: 978-0-9972972-0-1
Формат: PDF, DJVU
Страниц: 213
Размер: 10.82 МБ
Язык: English

The book's aim is to highlight all the complex issues, tasks and techniques that must be mastered by a SoC Architect to define and architect SoC for an embedded application. This book is primary focused on real problems with emphasis on architectural techniques across various aspects of chip-design, especially in context to embedded systems.

Название: Machine Learning: An Algorithmic Perspective, 2nd Edition
Автор: Stephen Marsland
Издательство: Chapman and Hall/CRC
Год: 2015
Страниц: 457
Формат: PDF
Размер: 10 Mb
Язык: English

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Название: Programming Massively Parallel Processors: A Hands-on Approach, 3rd Edition
Автор: David B. Kirk, Wen-mei W. Hwu
Издательство: Morgan Kaufmann
Год: 2017
Страниц: 576
Формат: PDF
Размер: 24 Mb
Язык: English

Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs.
Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth.
For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices.

Название: Pattern Classification
Автор: Richard O. Duda, Peter E. Hart, David G. Stork
Издательство: John Wiley & Sons
Год: 2012
Формат: PDF
Страниц: 738
Размер: 11.28 МБ
Язык: English

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances.

Название: Modern Data Access with Entity Framework Core: Database Programming Techniques for .NET, .NET Core, UWP, and Xamarin with C#
Автор: Holger Schwichtenberg
Издательство: Apress
ISBN: 1484235517
Год: 2018
Страниц: 665
Язык: английский
Формат: pdf (true), epub
Размер: 41.0 MB

C# developers, here’s your opportunity to learn the ins-and-outs of Entity Framework Core, Microsoft’s recently redesigned object-relational mapper. Benefit from hands-on learning that will teach you how to tackle frustrating database challenges, such as workarounds to missing features in Entity Framework Core, and learn how to optimize the performance of your applications, head-on! Modern Data Access with Entity Framework Core teaches best practices, guidance, and shortcuts that will significantly reduce the amount of resources you internally dedicate to programming data access code.

Название: Developing Windows Store Apps with HTML5 and javascript
Автор: Rami Sarieddine
Издательство: Packt Publishing
Год: 2013
Формат: PDF
Страниц: 184
Для сайта: vtome.ru
Размер: 4 Mb
Язык: English
The Windows store is growing in popularity and with this step-by-step guide it's easy to join the bandwagon using HTML5, CSS3, and jаvascript. From basic development techniques to publishing on the store, it's the complete primer.
Overview
Learn about the powerful new features in HTML5 and CSS3
Quick start a jаvascript app from scratch
Get your app into the store and learn how to add authentication

Название: Practical Node.js: Building Real-World Scalable Web Apps, 2nd Edition
Автор: Azat Mardan
Издательство: Apress
Год: 2018
Страниц: 505
Формат: PDF
Размер: 12 Mb
Язык: English

Learn how to build a wide range of scalable real-world web applications using a professional development toolkit. If you already know the basics of Node.js, now is the time to discover how to bring it to production level by leveraging its vast ecosystem of packages.With this book, you'll work with a varied collection of standards and frameworks and see how all those pieces fit together.

Название: Deep Learning for Numerical Applications with SAS
Автор: Henry Bequet
Издательство: SAS Institute
Год: 2018
Страниц: 234
Формат: True PDF, EPUB
Размер: 27 Mb
Язык: English

Dive into deep learning! Machine learning and deep learning are ubiquitous in our homes and workplaces-from machine translation to image recognition and predictive analytics to autonomous driving. Deep learning holds the promise of improving many everyday tasks in a variety of disciplines. Much deep learning literature explains the mechanics of deep learning with the goal of implementing cognitive applications fueled by Big Data.