Modern Frontend Development with Node.jsAlmost a decade after the release of Node.js, the tooling used by frontend developers is fully embracing this cross-platform JavaScript runtime, which is sadly often limited to server-side web development. This is where this Node.js book comes in, showing you what this popular runtime has to offer and how you can unlock its full potential to create frontend-focused web apps.
You'll begin by learning the basics and internals of Node.js, before discovering how to divide your code into modules and packages. Next, you'll get to grips with the most popular package managers and their uses and find out how to use TypeScript and other JavaScript variants with Node.js. Knowing which tool to use when is crucial, so this book helps you understand all the available state-of-the-art tools in Node.js. You'll interact with linters such as ESLint and formatters such as Prettier. As you advance, you'll become well-versed with the Swiss Army Knife for frontend developers - the bundler. You'll also ex ...
Java Memory ManagementUnderstanding how Java organizes memory is important for every Java professional, but this particular topic is a common knowledge gap for many software professionals. Having in-depth knowledge of memory functioning and management is incredibly useful in writing and analyzing code, as well as debugging memory problems. In fact, it can be just the knowledge you need to level up your skills and career.
In this book, you'll start by working through the basics of Java memory. After that, you'll dive into the different segments individually. You'll explore the stack, the heap, and the Metaspace. Next, you'll be ready to delve into JVM standard garbage collectors. The book will also show you how to tune, monitor and profile JVM memory management. Later chapters will guide you on how to avoid and spot memory leaks.
By the end of this book, you'll have understood how Java manages memory and how to customize it for the benefit of your applications. ...
Applying Math with Python, 2nd EditionThe updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.
You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.
By the end of this book, y ...
Learning SAP ABAPABAP (Advanced Business Application Programming) is a high-level programming language developed by SAP for programming business applications in the SAP environment.
It is an unofficial and free SAP ABAP book created for educational purposes. All the content is extracted from Stack Overflow Documentation, which is written by many hardworking individuals at Stack Overflow. ...
Data Visualization with Python and JavaScript, 2nd EditionHow do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts-as well as Python and JavaScript developers-how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries.
Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. ...
Raspberry Pi Cookbook, 4th EditionIf you've started to work with Raspberry Pi, you know that Raspberry Pi's capabilities are continually expanding. The fourth edition of this popular cookbook provides more than 200 hands-on recipes (complete with code) that show you how to run this tiny low-cost computer with Linux, program it with Python, hook it up to sensors and motors, and use it with the internet of things (IoT). This new edition includes new chapters on the Raspberry Pi Pico and machine learning with the Raspberry Pi.
These easy-to-use recipes will show you, step-by-step, how to: Set up your Raspberry Pi and connect to a network; Work with its Linux-based operating system; Program your Raspberry Pi with Python; Give your Pi "eyes" with computer vision; Recognize objects from video and sounds using machine learning; Control hardware through the GPIO connector; Use your Raspberry Pi to run different types of motors; Work with switches, keypads, and other digital inputs; Use sensors to measure temperature, light, ...
C++ TodayNow that software development is shifting primarily toward mobile and cloud computing, the venerable C++ programming language is returning to the dominant position it held during the object-oriented boom of the 1990s. In this O'Reilly report, you'll learn why C++ is once again the preferred choice across several diverse industries, after taking a backseat to Java during the 2000s.
C++ is a complicated beast that's not easy to learn. But when you need a powerful, highly portable systems programming language or an application programming language with uncompromising performance, it's hard to beat. With the 2011 and 2014 updates, C++ feels like a completely new language, rather than the old C++ with new features bolted on.
Authors Jon Kalb and Ga�per A�man demonstrate how modern C++ (C++11 and C++14) provides the power, performance, libraries, and tools necessary for massive server farms as well as low-footprint mobile apps.
- Delve into the modern C++ features that are gener ...
Productionizing AIThis book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app.
From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions.
The b ...
Beginning Spring DataUse the popular Spring Data project for data access and persistence using various Java-based APIs such as JDBC, JPA, MongoDB, and more.
This book shows how to easily incorporate data persistence and accessibility into your microservices, cloud-native applications, and monolithic enterprise applications. It also teaches you how to perform unit and performance testing of a component that accesses a database. And it walks you through an example of each type of SQL and NoSQL database covered.
After reading this book, you'll be able to create an application that interacts with one or multiple types of databases, and conduct unit and performance testing to analyze possible problems. ...
Advanced Data Analytics Using Python, 2nd EditionUnderstand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.
Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.
Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpa ...