Найти книгу: "Deep Learning for Numerical Applications with SAS"

Foreword by Oliver Schabenberger, PhD Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS 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. This book is different. Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS introduces a new field: Deep Learning for Numerical Applications (DL4NA). Contrary to deep learning, the primary goal of DL4NA is not to learn from data but to dramatically improve the performance of numerical applications by training deep neural networks. Deep Learning for Numerical Applications with SAS presents deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs: from many-core CPUs to GPUs to FPGAs to ASICs. This book assumes the reader has no prior knowledge of high-performance computing, machine learning, or deep learning. It is intended for SAS developers who want to develop and run the fastest analytics. In addition to discovering the latest trends in hybrid architectures with GPUs and FPGAS, readers will learn how to Use deep learning in SAS Speed up their analytics using deep learning Easily write highly parallel programs using the many task computing paradigms This book is part of the SAS Press program.

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Written by members of the Oracle XML group, this is a must-have reference for all IT managers, DBAs, and developers who want to learn the best practices for using XML with Oracle’s XML-enabled products. Includes real-world case studies based on theauthors’ experience managing Oracle’s XML Discussion Forum - a community of 20,000+ XML component users.

Rizzoni is designed for the sophomore/junior level, Introduction to Electrical Engineering course required for non-EE majors. The most widely used book for this course, Rizzoni introduces non-majors to the three basic areas of electrical engineering: circuits, electronics, and electromechanics. The second edition continues the tradition of focusing on the topics and issues of interest to the non-electrical engineering student. To maintain student interest in these topics, Rizzoni provides numerous links between electrical engineering and other engineering fields.

В работе представлено описание экспертных проектов по оценке качества образования. Рассмотрены типы организаций, осуществляющих такие проекты. Приведены также примеры отечественных и зарубежных экспертных проектов по оценке качества образования, реализуемого с применением e-learning.

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