This book is about understanding and analytical modeling of material layers, composites,artificial materials, and metamaterials

实用多元统计分析Applied Multivariate Statistical Analysis(Richard A. Johnson第4中文版) 数据分析与挖掘进阶书籍

《Matrix Analysis and Applied Linear Algebra》国外著名矩阵分析与应用的教材，研究生期间使用的该教材，深度广度都不错

应用线性回归模型 非常好的一本书，国外很多大学选作为回归分析课的教材，值得好好研读。

Applied Software Architecture 中文版，PDF，Christine Hofmeister

Estimtion is the process of extracting information from data - data which can be used to infer the desired information and niay contain errors. Modern estimation methods use known relationships to compute the deslred information from the measurements. taking account of measurement errors, the effects of disturbances and control actions on the system, and prior knowledge of the information. Diverse measurements car! be blended to form "best" estimates, and information which is unavailable for measurement can be approximated in an optimal fashion. The intent of this book is to enable readers to achieve a level of competence that will permit their participation in the des~gn and evaluation of practical estimators. Therefore, the text is oriented to the applied rather than theoretical aspects of optimal estimation. It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant,arguments are used extensively, with emphasis on physical insights and key questlons of practical importance.

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features x Explains neural networks in a multi-disciplinary context x Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting x Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA.

非常经典的统计学习书籍，英文书籍，影音版的，很清晰

这本是[Matrix Analysis and Applied Linear Algebra]配套习题集答案。

实现的一个图像缩放类,使用模板和STL技术,其中内存管理都是自己实现的，有异常处理类，很有参考价值。