Видавець: 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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Democratizing Application Development with AppSheet. A citizen developer's guide to building rapid low-code apps with the powerful features of AppSheet

Koichi Tsuji, Suvrutt Gurjar, Takuya Miyai

Testing with F#. Deliver high-quality, bug-free applications by testing them with efficient and expressive functional programming

Mikael Lundin

Mastering Kali Linux for Advanced Penetration Testing. This book will make you an expert in Kali Linux penetration testing. It covers all the most advanced tools and techniques to reproduce the methods used by sophisticated hackers. Full of real-world examples – an indispensable manual

Robert Beggs

C# Programming Cookbook. Quick fixes to your common C# programming problems, with a focus on C# 6.0

Dirk Strauss

IBM Rational Team Concert 2 Essentials. Improve your team productivity with Integrated Process, Planning, and Collaboration using Team Concert Enterprise Edition

T C Fenstermaker, TC Fenstermaker, Suresh Krishna, Suresh Madhuvarsu

Learning F# Functional Data Structures and Algorithms. Get started with F# and explore functional programming paradigm with data structures and algorithms

Adnan Masood

Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques

Kumar Abhishek, Dr. Mounir Abdelaziz

Building Data-Driven Applications with LlamaIndex. A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Andrei Gheorghiu