Bøger af Poornachandra Sarang

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.

A comprehensive guide to the design, development, usage, and syntax of BPEL. It provides detailed coverage of BPEL4WS, its syntax, and where, and how, it is used. The web services orchestration stack is also explained, including standards such as WS-Security, WS-Coordination, WS-Transaction, WS-Addressing, and others.

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.

An internationally renowned Java expert teaches experienced Java programmers how to take their skills to the top tier and maximize all the new features and tools in the latest Java release.

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.

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.