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Full-Stack Web Development with Vue.js and Node
Full-Stack Web Development with Vue.js and Node

Isomorphic JavaScript was the buzzword of the year 2017, allowing developers to utilize a single language throughout their web development stack and build cost-effective and scalable applications. MEVN is a one such modern web development stack consisting of web applications such as MongoDB, Express.js, Vue.js, and Node.js. Hands-On Full-Stack Web Development with Vue.js 2 and Node.js leverages the harmony of these technologies to help you create full-stack web applications. Starting with the core frameworks, this example-based guide explains all the key concepts of frameworks, how to set them up properly, and how to use popular modules to connect them together and make them work cohesively. You will learn all this with the help of real-world examples. In addition to this, you will be able to scaffold web application architecture, add an authentication layer, and develop the MVC structure to support the development of your application. You'll explore how to create data models for yo ...
C++ Reactive Programming
C++ Reactive Programming

Reactive programming is an effective way to build highly responsive applications with an easy-to-maintain code base. This book covers the essential functional reactive concepts that will help you build highly concurrent, event-driven, and asynchronous applications in a simpler and less error-prone way. C++ Reactive Programming begins with a discussion on how event processing was undertaken by different programming systems earlier. After a brisk introduction to modern C++ (C++17), you'll be taken through language-level concurrency and the lock-free programming model to set the stage for our foray into the Functional Programming model. Following this, you'll be introduced to RxCpp and its programming model. You'll be able to gain deep insights into the RxCpp library, which facilitates reactive programming. You'll learn how to deal with reactive programming using Qt/C++ (for the desktop) and C++ microservices for the Web. By the end of the book, you will be well versed with advanced ...
Learning Jupyter 5, 2nd Edition
Learning Jupyter 5, 2nd Edition

The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you've explored throu ...
Mastering Angular Components, 2nd Edition
Mastering Angular Components, 2nd Edition

Angular framework embraces a mature user interface (UI) component architecture, a powerful tool when developing scalable application interfaces. The simple and deterministic design of Angular components supports you in building large and scalable component-based applications. Mastering Angular Components covers a new holistic way of thinking about UI development, and helps you discover the power of Angular 6 components through various examples. The book starts with an introduction to component-based user interfaces and how Angular 6 manages this concept. Following a step-by-step approach, you will build a fully functional task-management application using Angular. You'll start with basic layout components, adding some core task-management components before completing the first draft of your application. You will then move on to working through a refactoring exercise in order to learn how to enhance the way you deal with states and data. Once you have got to grips with all this, you ...
R Deep Learning Essentials, 2nd Edition
R Deep Learning Essentials, 2nd Edition

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You'll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning ...
Python Artificial Intelligence Projects for Beginners
Python Artificial Intelligence Projects for Beginners

Artificial Intelligence (AI) is the newest technology that's being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be ...
Hands-On Robotics with JavaScript
Hands-On Robotics with JavaScript

There has been a rapid increase in the use of JavaScript in hardware and embedded device programming. JavaScript has an effective set of frameworks and libraries that support the robotics ecosystem. Hands-On Robotics with JavaScript starts with setting up an environment to program robots in JavaScript. Then, you will dive into building basic-level projects such as a line-following robot. You will walk through a series of projects that will teach you about the Johnny-Five library, and develop your skills with each project. As you make your way through the chapters, you'll work on creating a blinking LED, before moving on to sensors and other more advanced concepts. You will then progress to building an advanced-level AI-enabled robot, connect their NodeBots to the internet, create a NodeBots Swarm, and explore MQTT. By the end of this book, you will have gained hands-on experience in building robots using JavaScript. ...
D3.js Quick Start Guide
D3.js Quick Start Guide

D3.js is a JavaScript library that allows you to create graphs and data visualizations in the browser with HTML, SVG, and CSS. This book will take you from the basics of D3.js, so that you can create your own interactive visualizations, to creating the most common graphs that you will encounter as a developer, scientist, statistician, or data scientist. The book begins with an overview of SVG, the basis for creating two-dimensional graphics in the browser. Once the reader has a firm understanding of SVG, we will tackle the basics of how to use D3.js to connect data to our SVG elements. We will start with a scatter plot that maps run data to circles on a graph, and expand our scatter plot to make it interactive. You will see how you can easily allow the users of your graph to create, edit, and delete run data by simply dragging and clicking the graph. Next, we will explore creating a bar graph, using external data from a mock API. After that, we will explore animations and motion ...
Become a Python Data Analyst
Become a Python Data Analyst

Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python's most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this ...
Hands-On Markov Models with Python
Hands-On Markov Models with Python

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you'll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You'll also learn to apply HMM to image proces ...
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