Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniques Key FeaturesBuild microservices to perform data transformation and ML model serving in JavaScriptExplore what Danfo.js is and how it helps with data analysis and data visualizationCombine Danfo.js and TensorFlow.js for machine learningBook Description Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you'll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You'll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you'll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you'll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you'll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser. What you will learnPerform data experimentation and analysis with Danfo.js and DnotebookBuild machine learning applications using Danfo.js integrated with TensorFlow.jsConnect Danfo.js with popular database applications to aid data analysisCreate a no-code data analysis and handling system using internal librariesDevelop a recommendation system with Danfo.js and TensorFlow.jsBuild a Twitter analytics dashboard for sentiment analysis and other types of data insightsWho this book is for This book is for data analysts, data scientists, and JavaScript developers who want to create data-driven applications in the JavaScript/Node.js environment. Intermediate-level knowledge of JavaScript programming and data science using pandas is expected.

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Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch

Combining the demand for AI with the ubiquity of JavaScript was inevitable. With Google's TensorFlow.js framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on, end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js and other tools Use resources to convert, train, and manage machine learning data Start building and training your own training models from scratch Learn how to create your own image classification models Examine transfer learning: retraining an advanced model to perform a new task

Discover practical recipes to get to grips with Node.js concepts and programming models for delivering a scalable server-side for your applications Key FeaturesImplement practical solutions for scaling, securing, and testing your Node.js web apps effectivelyBuild and deploy scalable microservices architecture with the power of Node.js 14Discover techniques for debugging and testing Node.js applicationsBook Description A key technology for building web applications and tooling, Node.js brings JavaScript to the server enabling full-stack development in a common language. This fourth edition of the Node Cookbook is updated with the latest Node.js features and the evolution of the Node.js framework ecosystems. This practical guide will help you to get started with creating, debugging, and deploying your Node.js applications and cover solutions to common problems, along with tips to avoid pitfalls. You'll become familiar with the Node.js development model by learning how to handle files and build simple web applications and then explore established and emerging Node.js web frameworks such as Express.js and Fastify. As you advance, you'll discover techniques for detecting problems in your applications, handling security concerns, and deploying your applications to the cloud. This recipe-based guide will help you to easily navigate through various core topics of server-side web application development with Node.js. By the end of this Node book, you'll be well-versed with core Node.js concepts and have gained the knowledge to start building performant and scalable Node.js applications. What you will learnUnderstand the Node.js asynchronous programming modelCreate simple Node.js applications using modules and web frameworksDevelop simple web applications using web frameworks such as Fastify and ExpressDiscover tips for testing, optimizing, and securing your web applicationsCreate and deploy Node.js microservicesDebug and diagnose issues in your Node.js applicationsWho this book is for The book is for web developers who have knowledge of JavaScript and want to gain a broad understanding of Node.js concepts for server-side development.

"JavaScript is the language of the web. Originally developed for making browser-based interfaces more dynamic, it is now used for large-scale software projects of all kinds, including scientific visualization tools and data services. However, most researchers and data scientists have little or no experience with it. This book is designed to fill that void. It introduces readers to JavaScript's power and idiosyncrasies, and guides them through the key features of the modern version of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser"--

This edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address. It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation.