Programming permeates almost all aspects of our lives. This includes being active on social media, shopping online, and participating in virtual courses. It also includes driving a car and using many devices. This book will teach you the basics of programming using the Processing programming language and provide practice with logical, algorithmic thinking. It can provide insight into what is involved in producing the technical infrastructure of our world. While reading this book, you can build programs based on your own ideas, using images you create or acquire and making connections to activities you enjoy. The chapters in the book will demonstrate the process of programming, starting with formulating an idea, planning, building on past projects, and refining the work, similar to writing an essay or composing a song. This approach will guide you to make use of logic and mathematics to produce beautiful effects. The text contains an Appendix with an introduction to p5.js, a way to produce JavaScript programs with Processing functionality for drawing, images, and interactions to publish on the Web. The term for program in Processing is sketch. The Processing language has been constructed by visual artists using the Java programming language as a base. However, the sketches featured in this book and typical in Processing are far more than static drawings; they incorporate interaction, animation, video, audio, and accessing files on the local computer and on the Web. Technical features are introduced and explained in the context of complete examples: games (Snake, Hangman, jigsaw, slingshot), making a collage of family images and video clips, preparing directions for folding an origami model, rotating objects in 3D, and others. Programming is a fun, creative, expressive pursuit. It does require attention to details and can be frustrating. Still, there is very little that compares to the satisfaction of building a program out of nothing and making it work (or taking an existing program and fixing a problem, or adding a feature and making it better). Programming 101 is your gateway to making this happen. What You'll Learn Gain basic programming skills Build fun and creative programs Use files for making a holiday card or a sequence of instructions Combine videos, images, and graphics in a Processing sketch Connections to other programming languages, most especially Java and JavaScript Who This Book Is For Anyone who has been thinking about trying programming, or has tried, but needs assistance. Anyone who wants to use their own creativity and artistic talents. Anyone who wants to know what is involved in the technology that is present in our lives. Anyone who wants to know what is involved in the technology that is present in our lives.

"This is one of the best technical books I've ever read. The writing is so good, and it covers an incredible amount of knowledge. Hands down, this is the best reference material on using asyncio anywhere." - Kent R. Spillner Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library. Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading. about the technology The time demands of running code synchronously quickly overload standard Python and slow your programs to a crawl. Python's Asynchronous I/O library asyncio was built to solve these performance problems by making it easy to divide and schedule computational tasks so they can be run independently. asyncio concurrently handles multiple operations without a drop in throughput or responsiveness, making your apps lightning fast and easier to scale. about the book Python Concurrency with asyncio teaches you to write concurrent Python code that will boost the speed of your apps and APIs. The book demystifies asynchio's unique single-threaded concurrency model, giving you a behind-the-scenes understanding of the library and its new async/await syntax. Hard-to-grok concurrency topics are broken down into simple flowcharts so you can easily see how your coroutines and tasks are running. You'll learn to apply asyncio to solve common performance problems, such as batch database jobs, slow web servers, and scaling microservices. All examples you'll build are designed to be usable in the real world, including a clever command line SQL client that can run multiple slow queries at the same time. By the time you're done, you'll even be able to combine asyncio with traditional multiprocessing and multithreading techniques for huge improvements to performance. what's inside Use coroutines and tasks alongside async/await syntax to run code concurrently Build web APIs and make concurrency web requests with aiohttp Run thousands of SQL queries concurrently Create a map-reduce job that can process gigabytes of data concurrently Use threading with asyncio to mix blocking code with asyncio code about the reader For intermediate Python programmers. No previous experience of concurrency required. about the author Matthew Fowler has over 15 years of software engineering experience in roles from architect to engineering director. He has worked on Python codebases in the machine learning space, as well as led development of a Python-based ecommerce site with tens of millions of users.

Over the past two decades, the booming ecommerce and fintech industries have become a breeding ground for fraud. Organizations that conduct business online are constantly engaged in a cat-and-mouse game with these invaders. In this practical book, Gilit Saporta and Shoshana Maraney draw on their experience of fraud fighting to provide best practices, methodologies, and tools to help your organization detect and prevent fraud and other malicious activities. Data scientists, data analysts, and fraud analysts will learn how to identify and quickly respond to attacks. You'll get a comprehensive view of typical incursions as well as recommended detection analytic methods. Online fraud is constantly evolving. This book helps experienced researchers safely guide and protect their organizations in the ever-changing fraud landscape. With this book, you will: Examine current fraud attacks and learn how to mitigate them Find the right balance between preventing fraud and providing a smooth customer experience Share insights across multiple business areas, including ecommerce and banking Evaluate potential risks for a new vertical, market, or product Train and mentor teams by initiating hackathons and kickstarting brainstorming sessions Get a framework of fraud methods and fraud-fighting analytics

Term rewriting systems, which developed out of mathematical logic, consist of sequences of discrete steps where one term is replaced with another. Their many applications range from automatic theorem proving systems to computer algebra. This book begins with several examples, followed by a chapter on basic notions that provides a foundation for the rest of the work. First-order and higher-order theories are presented, with much of the latter material appearing for the first time in book form. Subjects treated include orthogonality, termination, lambda calculus and term graph rewriting. There is also a chapter detailing the required mathematical background.

The Rust programming language is consistent and does its best to avoid surprising the programmer. Like all languages, though, Rust still has its quirks. But these quirks present a teaching opportunity. In this book, you'll work through a series of brain teasers that will challenge your understanding of Rust. By understanding the gaps in your knowledge, you can become better at what you do and avoid mistakes. Many of the teasers in this book come from the author's own experience creating software. Others derive from commonly asked questions in the Rust community. Regardless of their origin, these brain teasers are fun, and let's face it: who doesn't love a good puzzle, right? What better way to exercise your brain and increase your Rust programming knowledge than with a collection of dynamic brain teasers? As you read through each of these puzzles and try to work out the answers, you'll not only learn about Rust's unique quirks and peculiarities, you'll also have loads of fun along the way. Dive right in and get started with example code and sample problems that cover numbers and text, shadowing and memory, and everything in between. Try to figure out why a particular program won't compile, why it produces unexpected output, or why it panics and terminates with an error message. Once you've run the code and read the answer, it's time to get to the heart of the matter with a detailed explanation. Learn why a program produced the result it did, and discover how similar issues might affect the code you write in your own programs, even in production. Sourced from engaging discussions within the Rust community, real-world problems, and even reader feedback, these challenges will certainly surprise, enlighten, and entertain you. Are you ready to experience Rust like never before? Then sharpen your brain and get ready for a challenge! What You Need: This book assumes you have some knowledge of the Rust programming language. To work through the brain teasers in this book, you'll need a working Rust environment on any platform. You can install Rust by visiting https: //rustup.rs/. You'll also need a text editor or Rust-friendly IDE.

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesus Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

C++ is capable of tackling a whole range of programming tasks. The purpose of this book is to give breadth and depth to C++ programmers’ existing experience of the language by representing a large number of algorithms, most of them implemented as ready-to-run (and standalone) programs. The programs are as readable as possible without sacrificing too great a degree of efficiency, generality, portability and robustness. Both the classes and programs are designed to demonstrate major programming principles. There is coverage of two key language features - templates and exception handling - apart from which the reader is assumed to have working knowledge of C++. Besides traditional subjects, such as quicksort and binary trees, this book also covers some less well-known topics, including multi-precision arithmetic, route planning and external sorting. Demonstration programs for these and many other exciting applications are based on C++ classes which you can also use in programs of your own.

Understand the concepts of image processing with Python 3 and create applications using Raspberry Pi 4. This book covers image processing with the latest release of Python 3, using Raspberry Pi OS and Raspberry Pi 4B with the 8 GB RAM model as the preferred computing platform. This second edition begins with the installation of Raspberry Pi OS on the latest model of Raspberry Pi and then introduces Python programming language, IDEs for Python, and digital image processing. It also illustrates the theoretical foundations of Image processing followed by advanced operations in image processing. You'll then review image processing with NumPy, and Matplotlib followed by transformations, interpolation, and measurements of images. Different types of filters such as Kernels convolution filters, low pass filters, high pass filters, and Fourier filters are discussed in a clear, methodical manner. Additionally, the book examines various image processing techniques such as Morphology, Thresholding, and Segmentation, followed by a chapter on live webcam input with OpenCV, an image processing library with Python. The book concludes with an appendix covering a new library for image processing with Python, pgmagik, followed by a few important tips and tricks relevant to RPi. What You'll Learn Get started with Raspberry Pi and Python Understand Image Processing with Pillow See how image processing is processed using Numpy and Matplotlib Use Pi camera and webcam Who This Book Is For Raspberry Pi and IoT enthusiasts, and Python and Open Source professionals

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app. About the Author Dr. Jesus Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn Understand the fundamental concepts of working with geodata Work with multiple geographical data types and file formats in Python Create maps in Python Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment

This volume contains the proceedings of the 17th International SPIN Workshop on Model Checking Software (SPIN 2010). The workshop was organized by and held at the University of Twente, The Netherlands, on 27-29 September 2010. The workshop was co-located with the 5th International Conference on Graph Transformation (ICGT 2010) and several of its satellite workshops, and with the joint PDMC and HiBi workshops, on Parallel and Distributed Methods for veri?Cation and on High-performance computational systems Biology. The SPIN workshopis a forum for practitioners and researchersinterested in state-spaceanalysisofsoftware-intensivesystems.Thisisapplicableinparticular to concurrent and asynchronous systems, including protocols. The name of the workshop re?ects the SPIN model checking tool by Gerard J. Holzmann, which won the ACM System Software Award 2001, and is probably the most widely used industrial-strength model checker around. The focus of the workshop is on theoretical advances and extensions, al- rithmic improvements, and empirical evaluation studies of (mainly) state-based modelcheckingtechniques,asimplementedintheSPINmodelcheckerandother tools. The workshop encourages interaction and exchange of ideas with all - lated areas in software engineering. To this end, we co-located SPIN 2010 with the graph transformation, and high-performance analysis communities. This year, we received 33 submissions, divided between 29 regular and 4 tool papers.Eachpaperwasrigorouslyreviewedbyatleastfourreviewers,andjudged onitsqualityandits signi?canceandrelevanceforSPIN.Weaccepted13regular papers, and 2 tool papers for presentation and for publication in this volume.

Software Quality Control, Error, Analysis

Emphasizing the detailed design of various Verilog projects, Verilog HDL: Digital Design and Modeling offers students a firm foundation on the subject matter. The textbook presents the complete Verilog language by describing different modeling constructs supported by Verilog and by providing numerous design examples and problems in each chapter. Examples include counters of different moduli, half adders, full adders, a carry lookahead adder, array multipliers, different types of Moore and Mealy machines, and much more. The text also contains information on synchronous and asynchronous sequential machines, including pulse-mode asynchronous sequential machines. In addition, it provides descriptions of the design module, the test bench module, the outputs obtained from the simulator, and the waveforms obtained from the simulator illustrating the complete functional operation of the design. Where applicable, a detailed review of the topic's theory is presented together with logic design principles, including state diagrams, Karnaugh maps, equations, and the logic diagram. Verilog HDL: Digital Design and Modeling is a comprehensive, self-contained, and inclusive textbook that carries all designs through to completion, preparing students to thoroughly understand this popular hardware description language.

Transition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software. This book provides a comprehensive overview of Streamlit, allowing developers and programmers of all backgrounds to get up to speed in as little time as possible. Streamlit is a pure Python web framework that will bridge the skills gap and shorten development time from weeks to hours. This book walks you through the complete cycle of web application development, from an introductory to advanced level with accompanying source code and resources. You will be exposed to developing basic, intermediate, and sophisticated user interfaces and subsequently you will be acquainted with data visualization, database systems, application security, and cloud deployment in Streamlit. In a market with a surplus demand for full stack developers, this skill set could not possibly come at a better time. In one sentence, Streamlit is a means for the empowerment of developers everywhere and all stand to gain from it. What You'll Learn Mutate big data in real-time Visualize big data interactively Implement web application security and privacy protocols Deploy Streamlit web applications to the cloud using Streamlit, Linux and Windows servers Who is this Book for? Developers with solid programming experience wanting to learn Streamlit; Back-end developers looking to upskill and transition to become a full-stack developers; Those who wish to learn and become more acquainted with data visualization, database systems, security and cloud deployment with Steamlit

Get started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers. What You Will Learn Apply artificial neural networks when modelling medical data Know the standard method for Markov decision making and medical data simulation Understand survival analysis methods for investigating data from a clinical trial Understand medical record categorization Measure personality differences using psychological models Who This Book Is For Machine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work setting

Business users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data. It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job. As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users. Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The first chapters are more Python-centric, while the remaining chapters illustrate SAS and corresponding Python examples to solve common data analysis tasks such as reading multiple input sources, missing value detection, imputation, merging/combining data, and producing output. This book is an indispensable guide for integrating SAS and Python workflows. What You'll Learn Quickly master Python for data analysis without using a trial-and-error approach Understand the similarities and differences between Base SAS and Python Better determine which language to use, depending on your needs Obtain quick results Who This Book Is For SAS users, SAS programmers, data scientists, data scientist leaders, and Python users who need to work with SAS

MATLAB: A Practical Introduction to Programming and Problem Solving, winner of TAA's 2017 Textbook Excellence Award ("Texty"), guides the reader through both programming and built-in functions to easily exploit MATLAB's extensive capabilities for tackling engineering and scientific problems. Assuming no knowledge of programming, this book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. The sixth edition has been updated to reflect the functionality of the current version of MATLAB (R2021a), including the introduction of machine learning concepts and the Machine Learning Toolbox, and new sections on data formats and data scrubbing.

vi and Vim are immensely powerful tools for anyone working with Unix, Linux, or Mac OS X, but there are far too many commands for anyone to remember. Author Arnold Robbins (vi and Vim Editors in a Nutshell) has chosen the most important and valuable commands for vi, Vim, and their main clones -- vile, elvis, and nvi -- and packed them into this convenient pocket reference. Fully updated and with greatly expanded Vim coverage, this handy little book puts all of the essential information about vi and Vim at your fingertips, in a format that makes browsing easy. You'll find commands to help you program, fix important system files, work on articles and other texts that use mark-up, and do any other kind of text editing. vi and Vim Editors Pocket Reference includes: * Command-line options * Command mode commands * Ex commands and options * Regular expressions * Other pertinent information for using vi and Vim * Additional features in vile, elvis, and nvi

Learning Maya, the world's leading 3D animation and effects package, is a challenge, especially for those who want to master Maya's versatile programming features in addition to its built-in tools.
Finally, here is a practical, step-by-step guide that shows how to use Maya to its fullest potential, beginning with the basics. Readers of Complete Maya Programming will first gain a thorough understanding of Maya's inner workings, and then learn how to customize and extend Maya with scripts and plugins that take control and productivity to new levels.
Users new to programming can apply Maya's easy scripting language MEL (Maya Embedded Language), while more advanced users can work with the C++ API (Application Progamming Interface). Both a fundamental tutorial for Maya beginners and a solid reference for experienced developers, Complete Maya Programming is every user's guide to Maya mastery.
* Provides a multitude of real-world examples illustrating applications of Maya programming.
* Demonstrates how to use MEL to control Maya, customize its interface, automate procedures, and more
* Details how to use the C++ API to modify Maya functionality and develop tools and features to meet any need
* Explains when to use MEL, when to use the C++ API, and how to use them together
* Ideal for technical directors, developers, or anyone wishing to to master Maya
* Provides a storehouse of MEL scripts and C++ source code, glossary, and list of resources, available at www.davidgould.com

Covering all the essential components of Unix/Linux, including process management, concurrent programming, timer and time service, file systems and network programming, this textbook emphasizes programming practice in the Unix/Linux environment. Systems Programming in Unix/Linux is intended as a textbook for systems programming courses in technically-oriented Computer Science/Engineering curricula that emphasize both theory and programming practice. The book contains many detailed working example programs with complete source code. It is also suitable for self-study by advanced programmers and computer enthusiasts. Systems programming is an indispensable part of Computer Science/Engineering education. After taking an introductory programming course, this book is meant to further knowledge by detailing how dynamic data structures are used in practice, using programming exercises and programming projects on such topics as C structures, pointers, link lists and trees. This book provides a wide range of knowledge about computer systemsoftware and advanced programming skills, allowing readers to interface with operatingsystem kernel, make efficient use of system resources and develop application software.It also prepares readers with the needed background to pursue advanced studies inComputer Science/Engineering, such as operating systems, embedded systems, databasesystems, data mining, artificial intelligence, computer networks, network security,distributed and parallel computing.

Learn GUI application development from the ground up by building simple projects that teach the fundamentals of using PyQt6. This 2nd edition includes updated code, programs, and new chapters to get you started using the newest version. Taking a practical approach, each chapter will gradually teach more advanced and diverse concepts to aid you in designing and customizing interesting and professional applications. You'll start by learning important concepts related to GUI development, and then jump right into building different and exciting projects in every chapter. Along the way, you'll discover new widgets, layouts, and other concepts that will help you to build better UIs. You'll also construct more elaborate GUIs, covering topics that include storing data using the clipboard, graphics and animation, support for SQL databases, multithreading applications, and building modern-looking interfaces. Using this knowledge, you'll be able to build a photo editor, games, a text editor, a working web browser, and an assortment of other GUIs. In the end, this book will guide you through the process of creating UIs to help you bring your own ideas to life. Find out what you need to begin making your own applications with PyQt! What You'll Learn Develop cross-platform UIs with PyQt and Python Use PyQt's many widgets and apply them by building real applications Build larger applications through a step-by-step approach and break the code into smaller chunks for deeper understanding Work with more complex applications in PyQt, covering SQL databases, multithreading, web browsers, and more Create modern-looking UIs with Qt Quick and QtQml using the latest version of PyQt Who This Book Is For Python developers who are looking to begin creating user interfaces and want to utilize the latest version of PyQt to gets started. Having prior knowledge of PyQt or other Python UI toolkits is not necessary to begin using this book.

Working with Specialized Language: a practical guide to using corpora introduces the principles of using corpora when studying specialized language.
The resources and techniques used to investigate general language cannot be easily adopted for specialized investigations. This book is designed for users of language for special purposes (LSP). Providing guidelines and practical advice, it enables LSP users to design, build and exploit corpus resources that meet their specialized language needs. Highly practical and accessible, the book includes exercises, a glossary and an appendix describing relevant resources and corpus-analysis software.
Working with Specialized Language is ideal for translators, technical writers and subject specialists who are interested in exploring the potential of a corpus-based approach to teaching and learning LSP.

Learn the basics of the Elm platform for web applications. This book covers the language as of version 0.18 and the most important libraries. After reading this book you will have an understanding what Elm can do for you. Also, you will be able to build on the example in the book to develop advanced web applications with Elm. What You'll Learn: Work with Elm and its development environment Learn the language and libraries in examples Use the Elm architecture to create applications with the Elm platform Put it all together with a sample application and explanation that covers the implementation details Who This Book Is For: Web developers new to Elm, with some experience in JavaScript recommended. This book is also for others curious about Elm and its potential beyond web development.