Poornachandra Sarang

We present the novel, modular Cross-Domain Trust Mesh (CDTM) for governance-aware systems that are safe, compatible, and decomposable. This book is intended for scholars, researchers, developers, and inquisitive learners.

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.

Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are. What You'll LearnDevelop Machine Learning ApplicationsTranslate languages using neural networksCompose images with style transferWho This Book Is ForBeginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies.

Pro Apache XML thoroughly introduces several subprojects found under the Web Services–driven API umbrella. The book begins with a concise introduction to XML and Web Services. Subsequent chapters devote themselves to specific subprojects covering their purposes, APIs, implementation, and practical uses through appropriate code examples. Additional material includes Introductions to several of the Apache Foundation's hottest projects, including Xerces, Axis, and Xindice Building XML–driven websites using the popular Cocoon project Transforming XML–based documents into a variety of formats, including PDF, SVG, and PS, using the Formatting Objects Processor (FOP) project You'lll learn to perform tasks like validation, parsing, transformation, and storage and how to work with this information through a programming interface. You may either read this book from cover to cover or jump to any desired subproject for a detailed study of its implementation. All programming examples use the Java programming language.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Develop, Compile, and Debug High-Performance Java ApplicationsTake your Java skills to the next level using the expert programming techniques contained in this Oracle Press guide. Featuring real-world code samples and detailed instructions, Java Programming demonstrates how to fully utilize the powerful features of Java SE 7. Find out how to design multithreaded and network applications, integrate structured exception handling, use Java libraries, and develop Swing-based GUIs and applets. Inheritance, generics, and utility classes are are covered in this practical resource. Create custom classes, methods, arrays, and operatorsControl program flow using conditional statementsHandle multithreaded, network, and I/O programmingLearn new constructs in multithreadingIncorporate enums, annotations, and autoboxingRecover from errors, input failures, and exceptionsUse Java Swing to build lightweight GUIs and appletsCut development time using the collections frameworkWork with the latest Java libraries and utility classes

Liferay Portal is the leading open-source enterprise portal framework that uses Java and Web 2.0 technologies. Web portals often function as a point of access to information on the World Wide Web. Web portals, such as Yahoo!, present information from diverse sources in a unified way. Aside from the search engine standard, web portals offer other services such as e–mail, news, stock prices, infotainment, and various other features. Portals provide a means for enterprises to supply a consistent look and feel with access control and procedures for multiple applications that otherwise would have been separate entities altogether. So, how do developers bring existing applications, as well as integrate content management systems and search engines, into a portal? And how do developers get started with the Liferay Portal engine? In Practical Liferay: Java–based Portal Applications Development, Dr. Poornachandra Sarang answers these questions and more. This book is for those who want to learn how to use Liferay to develop vertical or company–specific web portals and beyond. The book will serve as a practical guide to learning Liferay and developing real–world web portals.

Pro Apache XML thoroughly introduces several subprojects found under the Web Services–driven API umbrella. The book begins with a concise introduction to XML and Web Services. Subsequent chapters devote themselves to specific subprojects covering their purposes, APIs, implementation, and practical uses through appropriate code examples. Additional material includes Introductions to several of the Apache Foundation's hottest projects, including Xerces, Axis, and Xindice Building XML–driven websites using the popular Cocoon project Transforming XML–based documents into a variety of formats, including PDF, SVG, and PS, using the Formatting Objects Processor (FOP) project You'lll learn to perform tasks like validation, parsing, transformation, and storage and how to work with this information through a programming interface. You may either read this book from cover to cover or jump to any desired subproject for a detailed study of its implementation. All programming examples use the Java programming language.

This book is aimed at architects and developers in the design, implementation, and integration phases of advanced information systems and e-business solutions, developing business processes and dealing with the issues of composition, orchestration, transactions, coordination, and security. The book presumes knowledge of XML and web services, web services development (either on J2EE or . NET), and multi-tier architecture