To properly function in today’s work environment, engineers require a working familiarity with numerical analysis. This book provides that necessary background, striking a balance between analytical rigor and an applied approach focusing on methods particular to the solving of engineering...

• Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
• Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
• Build models relating to neural...

This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit...

Extensively class tested, this text takes an innovative approach to explaining the process of software testing: it defines testing as the process of applying a few well-defined, general-purpose test criteria to a structure or model of the software. The structure of the text directly reflects the pedagogical approach and incorporates the latest...

• Focuses on building high quality industrial strength software in Java
• Ready-to-use source code is given throughout the text based on industrial-strength projects undertaken by...

Essentials of Research Design and Methodology contains practical information on how to design and conduct scientific research in the behavioral and social sciences. This accessible guide covers basic to advanced concepts in a clear, concrete, and readable...

You can't “test quality into” a software product, but neither can you build a quality software product without test and analysis.  Software test and analysis is increasingly recognized, in research and in industrial practice, as a core challenge in...