Wydawca: Packt Publishing
Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools. As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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Learning AirWatch. Learn to implement, manage, and deploy the latest Enterprise Mobility Management (EMM) platform offered by AirWatch

Mark Dunkerley, Mark Dunkerley

SvelteKit Up and Running. Leverage the power of a next-generation web framework to build high-performance web apps with ease

Dylan Hildenbrand

Supercharge Your Applications with GraalVM. Hands-on examples to optimize and extend your code using GraalVM's high performance and polyglot capabilities

A B Vijay Kumar

Introduction to R for Quantitative Finance. R is a statistical computing language that's ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. Ideal for R beginners or expert alike

Gergely Daróczi, Michael Puhle, Edina Berlinger (EURO), Daniel Daniel Havran, ...

Ruby on Rails Web Mashup Projects. A step-by-step tutorial to building web mashups

Chang Sau Sheong, David Heinemeier Hansson

Learning Geospatial Analysis with Python. Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7 - Third Edition

Joel Lawhead

Full-Stack Web Development with TypeScript 5. Craft modern full-stack projects with Bun, PostgreSQL, Svelte, TypeScript, and OpenAI

Mykyta Chernenko, Artem Korchunov

Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks

Jay Dawani