Normalization Examples
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Normalization Techniques in Deep Learning
This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.
Normalization and Prediction of Geotechnical Properties Using the Cone Penetrometer Test (CPT)
This research was to develop techniques for (1) stress normalization of CPT measurements (and geotechnical properties) and (2) CPT prediction of geotechnical properties using cone and sleeve friction resistance values. Stress normalization allows a variable geotechnical property to be reduced to an equivalent value at a standard confining stress. A new concept, the Stress Focus, was identified which provides a basis for understanding soil strength as a function of confining stress. This study demonstrated that sand friction angles for different initial relative densities converge to a Stress Focus at high confining stress (approximately 100 atm), where the strength behavior is similar to that of a sedimentary rock. Dilation of dense sands decreases with increased confining stress until the Stress Focus is reached, as confirmed using historic high pressure triaxial test data as well with CPT measurements from laboratory chamber tests and uniform soil layers. The paths of convergence to the Stress Focus are exponentially related to confining stress and are the basis for development of CPT cone and sleeve friction resistance normalization techniques. The overburden stress at the Stress Focus is soil type dependent. The stress exponent for SPT normalization was shown to be equal to the CPT derived stress exponent. CPT correlations to geotechnical properties were established using both CPT cone resistance and friction ratio. Geotechnical properties, Stress exponent, Sand friction angles, Stress normalization, Soil strength.
Inside Relational Databases with Examples in Access
Author: Mark Whitehorn
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-04-06
Contents Should we tell you the whole story? Of course, there is an inevitable tension in trying to work like this. For example, in Chapter 16 we talk about referential integrity. There are - sentially six different flavors of referential integrity but Access only s- ports four of them (they are the most important ones however, so you aren’t missing out on too much). The problem is this. Should we tell you about the other two? If we do, as an Access user you have every right to be annoyed that we are telling you about a feature you can’t use. On the other hand, the six different types that we describe are part of the re- tional world and this book is about that world – we are not trying to teach you how to use Access, we are simply using Access to illustrate the relational model. Ultimately we decided to risk your ire and to describe all of the features of the relational model as we see it, even if Access doesn’t support all of them. One advantage of this approach is that if you need to use a different database engine you will almost certainly find the extra information useful. Incidentally, this is not meant to imply that Access is somehow lacking as a relational database engine. The reason we chose it for the first book is that it is such a good example of a relational database tool.