In just 24 lessons of one hour or less, Coding with Roblox Lua in 24 Hours: The Official Roblox Guide helps you learn all the skills and techniques you'll need to code your own Roblox experiences. Perfect for beginners, each short and easy lesson builds upon everything that's come before, helping you quickly master the essentials of Lua programming. Step-by-step instructions walk you through common questions, issues, and tasks; Q&As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. Learn how to... * Code with properties, variables, functions, if/then statements, and loops * Organize information using arrays and dictionaries * Work with events to make things move, explode, count down, and do whatever you can imagine * Keep your code manageable with abstractions and object-oriented programming * Store data permanently to create leaderboards, inventories, and custom currency * Use raycasting to allow visitors to place their own objects, such as furniture and props, within your world

Master Today's Best Practices for Building Reusable .NET Frameworks, Libraries, and Components ".NET Core [contains] advances important to cloud application developers: performance, resource utilization, container support, and others. This third edition of Framework Design Guidelines adds guidelines related to changes that the .NET team adopted during transition from the world of client-server application to the world of the Cloud." -From the Foreword by Scott Guthrie Framework Design Guidelines has long been the definitive guide to best practices for developing components and component libraries in Microsoft .NET. Now, this third edition has been fully revised to reflect game-changing API design innovations introduced by Microsoft through eight recent updates to C#, eleven updates to .NET Framework, and the emergence and evolution of .NET Core. Three leading .NET architects share the same guidance Microsoft teams are using to evolve .NET, so you can design well-performing components that feel like natural extensions to the platform. Building on the book's proven explanatory style, the authors and expert annotators offer insider guidance on new .NET and C# concepts, including major advances in asynchronous programming and lightweight memory access. Throughout, they clarify and refresh existing content, helping you take full advantage of best practices based on C# 8, .NET Framework 4.8, and .NET Core. Discover which practices should always, generally, rarely, or never be used-including practices that are no longer recommended Learn the general philosophy and fundamental principles of modern framework design Explore common framework design patterns with up-to-date C# examples Apply best practices for naming, types, extensibility, and exceptions Learn how to design libraries that scale in the cloud Master new async programming techniques utilizing Task and ValueTask Make the most of the Memory and Span types for lightweight memory access This guide is an indispensable resource for everyone who builds reusable .NET-based frameworks, libraries, or components at any scale: large system frameworks, medium-size reusable layers of large distributed systems, extensions to system frameworks, or even small shared components. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

This in-depth guide covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. In this book, you'll implement numerical algorithms in Kotlin using NM Dev, an object-oriented and high-performance programming library for applied and industrial mathematics. Discover how Kotlin has many advantages over Java in its speed, and in some cases, ease of use. In this book, you'll see how it can help you easily create solutions for your complex engineering and data science problems. After reading this book, you'll come away with the knowledge to create your own numerical models and algorithms using the Kotlin programming language. What You Will Learn Program in Kotlin using a high-performance numerical library Learn the mathematics necessary for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solutions Build solvers for industrial optimization problems Perform data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.

A system for statistical computing and dynamic graphics, based on the LISP language, is described in this book, which shows how to use the system for statistical calculations and graphs. The computations supported range from summary statistics through fitting linear and nonlinear regression models to general maximum likelihood estimation and approximate Bayesian computations. Standard graphs include scatter plots, rotatable plots and scatterplot matrices. No prior knowledge of LISP is assumed; the basics are introduced as they are needed. Several chapters include extensive examples.

Kafka in Action is a practical, hands-on guide to building Kafka-based data pipelines. Filled with real-world use cases and scenarios, this book probes Kafka's most common use cases, ranging from simple logging through managing streaming data systems for message routing, analytics, and more. In systems that handle big data, streaming data, or fast data, it's important to get your data pipelines right. Apache Kafka is a wicked-fast distributed streaming platform that operates as more than just a persistent log or a flexible message queue. Key Features * Understanding Kafka's concepts * Implementing Kafka as a message queue * Setting up and executing basic ETL tasks * Recording and consuming streaming data * Working with Kafka producers and consumers from Java applications * Using Kafka as part of a large data project team * Performing Kafka developer and admin tasks Written for intermediate Java developers or data engineers. No prior knowledge of Kafka is required. About the technology Apache Kafka is a distributed streaming platform for logging and streaming data between services or applications. With Kafka, it's easy to build applications that can act on or react to data streams as they flow through your system. Operational data monitoring, large scale message processing, website activity tracking, log aggregation, and more are all possible with Kafka. Dylan Scott is a software developer with over ten years of experience in Java and Perl. His experience includes implementing Kafka as a messaging system for a large data migration, and he uses Kafka in his work in the insurance industry.

A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis SQL and Excel to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS. * Understand core analytic techniques that work with SQL and Excel * Ensure your analytic approach gets you the results you need * Design and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.

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.