Written by two very experienced instructors, with more than thirty years of teaching experience between them; Presents material that is grounded in practical applications that are representative of the problems researchers encounter in real life; Teaches readers the core features of modern JavaScript; Covers programming with callbacks and promises; Describes how to build data services and data visualization;

Unlike other resources that target only programming communities, this book targets both programming and business communities. With programming models shifting more towards no-code and low-code, citizen programmers from the business side will welcome this book as a guide for how to design and optimize their information pipeline while lowering costs for infrastructure. Programmers, on the other hand, will welcome this book's business-centric programming view, which will get them a step closer to fulfilling real business requirements. Practical Spring Cloud Function touches on the themes of portability, scalability, high performance and high availability. Each theme is explored via a real enterprise use case and code. The use cases target industries including energy (oil pipeline sensors), automotive (event-driven connected vehicles), and retail (conversational AI). After reading this book, you'll come away with the know-how to build and deploy cloud-native Java applications effectively and efficiently. What You Will Learn Write functions and deploy to Amazon Web Services, Microsoft Azure, Google Cloud, IBM Cloud, and on-prem clouds such as VMWare Tanzu and RedHat OpenShift Set up locally with KNative on Kubernetes, as well as on AWS, Azure, GCP, Tanzu, and others Build, test, and deploy a simple example with Spring Cloud Function Develop an event-driven data pipeline with Spring Cloud Function Integrate with AI and machine learning models Apply Spring Cloud Function to the Internet of Things (IoT) Get industry-specific examples of Spring Cloud Function in action Who This Book Is For Software and cloud-native application developers with prior programming experience in the cloud and/or Spring Framework. DevOps professionals may find this book beneficial as well.

Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. What You Will Learn Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models Who This Book Is For AI engineers, data scientists, and software developers interested in XAI

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. What You Will Learn How to start developing web applications using Streamlit What are Streamlit's components Media elements in Streamlit How to visualize data using various interactive and dynamic Python libraries How to implement models in Streamlit web applications Who This Book Is ForProfessionals working in data science and machine learning domains who want to showcase and deploy their work in a web application with no prior knowledge of web development.

DLP denotes a dynamic-linear modeling and optimization approach to computational decision support for resource planning problems that arise, typically, within the natural resource sciences and the disciplines of operations research and operational engineering. It integrates techniques of dynamic programming (DP) and linear programming (LP) and can be realized in an immediate, practical and usable way. Simultaneously DLP connotes a broad and very general modeling/ algorithmic concept that has numerous areas of application and possibilities for extension. Two motivating examples provide a linking thread through the main chapters, and an appendix provides a demonstration program, executable on a PC, for hands-on experience with the DLP approach.

Spring Framework 6 remains - by far - the leading de-facto "out of the box" practical Java meta application development framework for building complex enterprise, cloud-native applications as well as web applications and microservices. Introducing Spring Framework 6 is your hands-on tutorial guide for learning the Spring Framework 6 from top to bottom, and allows you to build an example application along the way from the ground-up. As you learn the Spring Framework over the course of this book, you'll incrementally build your first Spring application piece-by-piece as you learn each module, project or component of the Spring Framework and its extensions and ecosystem. As you learn the various fundamentals, you'll then apply them immediately to your Spring application. This Spring application, My Documents, enables you to learn by doing. After reading this book, you will have the essentials you should need to start using the Spring Framework and building your own Java-based applications or microservices with it. What you'll learn: Get started with Spring Framework 6 by VMWare Tanzu and the Spring community Build your first My Documents application using Spring Framework and its extensions Test your Spring application Add persistence to your application using Spring Data JPA and more Show your Spring application on the Web with Spring MVC and related Use REST APIs to enhance your application and add messaging with Kafka and AMQP Integrate your Spring application with external systems using Spring Integration toolkit Who is this book for: This book is for those aspiring software developers and programmers who are new to Spring. Some prior programming experience recommended, preferably in Java.

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.

Get started quickly with Qt, the popular open source C++ framework for building C++-based applications and games. This book will have you building both fully functional desktop and mobile applications in no time, including some simple game applications. Introducing Qt 6 begins by guiding you in setting up your tools and environment, and then walks you through the first "baby steps" of Qt framework. Next, you'll learn the basics of how project and app structure are set up using Qt. Then, you'll begin your first real hands-on projects using Qt, including a task and problem management application and two games. As you progress, you can enhance these apps and games using additional Qt components and features. The book then delves into advanced topics in Qt, learning above and beyond what the Qt docs can offer, including local storage, C++ integration, deployment to Windows and Android, custom components and how to work with them. Upon completing this book, you'll come away knowing how to build a C++ application from design to deployment, top to bottom. And, you'll have actual application and game examples that you can apply to your own work or hobby. What You Will Learn Learn to build your first applications and games using Qt 6 framework Design, create, build and deploy your first Qt applications or games as finished products Explore local storage integration in theory and practice Cover deployment on Windows and on Android Integrate with C++ language to leverage additional functionality Dive into Custom Components and how to work with them Explore different project structures and more Who This Book Is For Software programmers, developers who are new to C++ or the Qt framework. Some prior programming experience though may be helpful.

Developed from the author's many years of teaching computing courses, Programming in C++ for Engineering and Science guides students in designing programs to solve real problems encountered in engineering and scientific applications. These problems include radioactive decay, pollution indexes, digital circuits, differential equations, Internet addresses, data analysis, simulation, quality control, electrical networks, data encryption, beam deflection, and many other areas. To make it easier for novices to develop programs, the author uses an object-centered design approach that helps students identify the objects in a problem and the operations needed; develop an algorithm for processing; implement the objects, operations, and algorithm in a program; and test, correct, and revise the program. He also revisits topics in greater detail as the text progresses. By the end of the book, students will have a solid understanding of how C++ can be used to process complex objects, including how classes can be built to model objects. Web ResourceThe book's website at http://cs.calvin.edu/books/c++/engr-sci provides source code, expanded presentations, links to relevant sites, reference materials, lab exercises, and projects. For instructors, solutions to exercises and PowerPoint slides for classroom use are available upon qualifying course adoption.

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.

With multicore processors now in every computer, server, and embedded device, the need for cost-effective, reliable parallel software has never been greater. By explaining key aspects of multicore programming, Fundamentals of Multicore Software Development helps software engineers understand parallel programming and master the multicore challenge. Accessible to newcomers to the field, the book captures the state of the art of multicore programming in computer science. It covers the fundamentals of multicore hardware, parallel design patterns, and parallel programming in C++, .NET, and Java. It also discusses manycore computing on graphics cards and heterogeneous multicore platforms, automatic parallelization, automatic performance tuning, transactional memory, and emerging applications. As computing power increasingly comes from parallelism, software developers must embrace parallel programming. Written by leaders in the field, this book provides an overview of the existing and up-and-coming programming choices for multicores. It addresses issues in systems architecture, operating systems, languages, and compilers.

Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker

This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You'll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you'll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare. What You Will Learn Understand the components required to facilitate precision health and personalized care Apply and implement precision health systems Overcome the challenges of delivering precision healthcare at scale Reconcile ethical and moral implications of delivering precision healthcare Gain insight into the hurdles providers face while implementing precision healthcare Who This Book Is For Healthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students

This print textbook is available for you to rent for your classes. The Pearson print rental program provides you with affordable access to learning materials, so you go to class ready to succeed. C How to Program is a user-friendly, code-intensive introduction to C programming with case studies introducing applications and system programming. Like other texts of the Deitels' How to Program series, the book's modular presentation serves as a detailed beginner source of information for college students looking to embark on a career in coding, or instructors and software-development professionals seeking to learn how to program with C. The signature Deitel live-code approach presents concepts in the context of 142 full-working programs rather than incomplete snips of code. This gives you a chance to run each program as you study it and see how your learning applies to real-world programming scenarios. Current standards, contemporary practice, and hands-on learning opportunities are integrated throughout the 9th Edition. Over 340 new integrated Self-Check exercises with answers allow you to test your understanding of important concepts - and check your code - as you read. New and enhanced case studies and exercises use real-world data and focus on the latest ACM/IEEE computing curricula recommendations, highlighting security, data science, ethics, privacy, and performance concepts.

Agile Systems Engineering presents a vision of systems engineering where precise specification of requirements, structure, and behavior meet larger concerns as such as safety, security, reliability, and performance in an agile engineering context. World-renown author and speaker Dr. Bruce Powel Douglass incorporates agile methods and model-based systems engineering (MBSE) to define the properties of entire systems while avoiding errors that can occur when using traditional textual specifications. Dr. Douglass covers the lifecycle of systems development, including requirements, analysis, design, and the handoff to specific engineering disciplines. Throughout, Dr. Douglass couples agile methods with SysML and MBSE to arm system engineers with the conceptual and methodological tools they need to avoid specification defects and improve system quality while simultaneously reducing the effort and cost of systems engineering.

"To the best of my knowledge, D offers an unprecedentedly adroit integration of several powerful programming paradigms: imperative, object-oriented, functional, and meta."
--From the Foreword by Walter Bright
"This is a book by a skilled author describing an interesting programming language. I'm sure you'll find the read rewarding."
--From the Foreword by Scott Meyers D is a programming language built to help programmers address the challenges of modern software development. It does so by fostering modules interconnected through precise interfaces, a federation of tightly integrated programming paradigms, language-enforced thread isolation, modular type safety, an efficient memory model, and more."The D Programming Language "is an authoritative and comprehensive introduction to D. Reflecting the author's signature style, the writing is casual and conversational, but never at the expense of focus and precision. It covers all aspects of the language (such as expressions, statements, types, functions, contracts, and modules), but it is much more than an enumeration of features.Inside the book you will findIn-depth explanations, with idiomatic examples, for all language featuresHow feature groups support major programming paradigmsRationale and best-use advice for each major featureDiscussion of cross-cutting issues, such as error handling, contract programming, and concurrencyTables, figures, and "cheat sheets" that serve as a handy quick reference for day-to-day problem solving with DWritten for the working programmer, "The D Programming Language "not only introduces the D language--it presents a compendium of good practices and idioms to help both your coding with D and your coding in general.

This book successfully balances the introduction of object-oriented concepts with data structures in C++.
KEY TOPICS: Provides interfaces for the principal example classes, which are compliant with the ANSI/ISO C++ Standard Library classes. Thorough coverage of the role of the const keyword in the C++ Standard Library. Covers C++ features such as namespaces, static member constants, typename keyword, and inheritance. Thorough review of C++ syntax and OOP concepts, make the book accessible for programmers at various levels. The book also gives readers a firm grasp of key concepts and allows programmers experienced in another language to adjust easily. A solid foundation in building and using abstract data types is also provided, along with an assortment of advanced topics such as B-trees for project building and graphs.
This book is designed for novice programmers who have learned the concepts of objects and classes and want to move on to the data structures topics of recursion and data abstraction.

Currently, we see a variety of tools and techniques for specifying and implementing business processes. The problem is that there are still gaps and tensions between the different disciplines needed to improve business process execution and improvement in enterprises. Business process modeling, workflow execution and application programming are examples of disciplines that are hosted by different communities and that emerged separately from each other. In particular, concepts have not yet been fully elaborated at the system analysis level. Therefore, practitioners are faced again and again with similar questions in concrete business process projects: Which decomposition mechanism to use? How to find the correct granularity for business process activities? Which implementing technology is the optimal one in a given situation? This work offers an approach to the systematization of the field. The methodology used is explicitly not a comparative analysis of existing tools and techniques - although a review of existing tools is an essential basis for the considerations in the book. Rather, the book tries to provide a landscape of rationales and concepts in business processes with a discussion of alternatives.

If you need to parse or process text data in Linux or Unix, this classic book explains how to use flex and bison to solve your problems quickly - whether you're interpreting code, configuration files, or any other structured format. "Flex and Bison" is the long-awaited sequel to the classic O'Reilly book, "Lex and Yacc". In the nearly two decades since that book was published, the "Flex and Bison" utilities have proven to be more reliable and more powerful than the original Unix tools. This book covers the same core functionality vital to Linux and Unix program development, along with several important new topics. This thoroughly updated edition will help you: address syntax crunching that regular expressions tools can't handle; build compilers and interpreters, and handle a wide range of text processing functions; learn key programming techniques, including syntax trees and symbol tables; implement a full SQL grammar, with complete sample code; and, use new features such as pure (reentrant) lexers and parsers, powerful GLR parsers, and interfaces to C++. This book includes revised tutorial sections for novice users and reference sections for advanced users, with chapters that explain each utility's basic usage and simple, stand-alone applications. Dive into "Flex and Bison" and discover the wide range of uses these flexible tools provide.

The art, craft, discipline, logic, practice and science of developing large-scale software products needs a professional base. The textbooks in this three-volume set combine informal, engineeringly sound approaches with the rigor of formal, mathematics-based approaches.

This volume covers the basic principles and techniques of specifying systems and languages. It deals with modelling the semiotics (pragmatics, semantics and syntax of systems and languages), modelling spatial and simple temporal phenomena, and such specialized topics as modularity (incl. UML class diagrams), Petri nets, live sequence charts, statecharts, and temporal logics, including the duration calculus. Finally, the book presents techniques for interpreter and compiler development of functional, imperative, modular and parallel programming languages.

This book is targeted at late undergraduate to early graduate university students, and researchers of programming methodologies. Vol. 1 of this series is a prerequisite text.

Security is too important to be left in the hands of just one department or employee-it's a concern of an entire enterprise. Enterprise Security Architecture shows that having a comprehensive plan requires more than the purchase of security software-it requires a framework for developing and maintaining a system that is proactive. The book is based around the SABSA layered framework. It provides a structured approach to the steps and processes involved in developing security architectures. It also considers how some of the major business issues likely to be encountered can be resolved.

Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Taking a task-based approach, you'll learn how to build features that use powerful AI features to identify images, make predictions, generate content, recommend things, and more. AI is increasingly essential for every developer-and you don't need to be a data scientist or mathematician to take advantage of it in your apps. Explore Swift-based AI and ML techniques for building applications. Learn where and how AI-driven features make sense. Inspect tools such as Apple's Python-powered Turi Create and Google's Swift for TensorFlow to train and build models. I: Fundamentals and Tools-Learn AI basics, our task-based approach, and discover how to build or find a dataset. II: Task Based AI-Build vision, audio, text, motion, and augmentation-related features; learn how to convert preexisting models. III: Beyond-Discover the theory behind task-based practice, explore AI and ML methods, and learn how you can build it all from scratch... if you want to

Learn to program with Rust 2021 Edition, in an easy, step-by-step manner on Unix, the Linux shell, macOS, and the Windows command line. As you read this book, you'll build on the knowledge you gained in previous chapters and see what Rust has to offer. Beginning Rust starts with the basics of Rust, including how to name objects, control execution flow, and handle primitive types. You'll see how to do arithmetic, allocate memory, use iterators, and handle input/output. Once you have mastered these core skills, you'll work on handling errors and using the object-oriented features of Rust to build robust Rust applications in no time. Only a basic knowledge of programming in C or C++ and familiarity with a command console are required. After reading this book, you'll be ready to build simple Rust applications. What You Will Learn Get started programming with Rust Understand heterogeneous data structures and data sequences Define functions, generic functions, structs, and more Work with closures, changeable strings, ranges and slices Use traits and learn about lifetimes Who This Book Is For Those who are new to Rust and who have at least some prior experience with programming in general: some C/C++ is recommended particularly.