Tables of Ordinary And Extraordinary Refractive Indices Group Refractive Indices And Hoxfcurves For Standard Ionospheric Layer Models

Discover deep learning and computer vision with SAS!
Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.

This book explores the Italian stock exchange through its construction and consolidation while examining and criticizing the birth of the capital city. Through the evolution of the stock exchange, the transformation of Rome is examined from the capital of a pre-unification state to the papal state, exploring its social, political, administrative and financial fabric. The book examines that path to becoming the capital of Italy, offering a unique volume for researchers, academics, and students of financial history and financial markets.

This book explores the notion of interculturality in education and supports scholars in their discovery of the notion. Continuing the author’s previous work, the book urges (communication) education researchers and educators to 'interculturalize' interculturality. This book corresponds to the authors’ endeavor to complexify the way interculturality is discussed, expressed, (co-)constructed and advocated in different parts of the world and in different languages.

Banks are exposed to various kinds of risks; among them are credit default risks, market price risks and operational risks the most important ones. Aggregating these different risk ex- sures to a comprehensive risk position is an important, yet challenging and up to now un- solved task. Banks’ current state of the art in risk management is still far away from achieving a fully integrated view of the risks they are exposed to. This shortfall traces back to both, to conceptual problems of constructing an appropriate risk model and to the computational b- den of calculating a loss distribution. The approach presented in this book takes credit default risk as a starting point. By integrating market risks, a general credit risk model is constructed that comprises the standard industry credit risk models as special cases. Within the framework of this general credit risk model, the effects of simplifying assumptions that are typical for standard credit risk models can be a- lyzed. Important insights gained by this analysis are that neglecting market price risks and losses given default correlated to default rates can cause a significant understatement of value at risk figures.