Building a Pentesting Lab for Wireless Networks. Build your own secure enterprise or home penetration testing lab to dig into the various hacking techniques

Andrey Popov, Vyacheslav Fadyushin, Aaron Woody

Starting with the basics of wireless networking and its associated risks, we will guide you through the stages of creating a penetration testing lab with wireless access and preparing your wireless penetration testing machine.This book will guide you through configuring hardware and virtual network devices, filling the lab network with applications and security solutions, and making it look and work like a real enterprise network. The resulting lab protected with WPA-Enterprise will let you practice most of the attack techniques used in penetration testing projects. Along with a review of penetration testing frameworks, this book is also a detailed manual on preparing a platform for wireless penetration testing. By the end of this book, you will be at the point when you can practice, and research without worrying about your lab environment for every task.

Building a Recommendation System with R. Learn the art of building robust and powerful recommendation engines using R

SURESH K GORAKALA, Michele Usuelli, Suresh Kumar Gorakala

Building a RESTful Web Service with Spring. A hands-on guide to building an enterprise-grade, scalable RESTful web service using the Spring Framework

Ludovic Dewailly

Building a Web Application with PHP and MariaDB: A Reference Guide. Build fast, secure, and interactive web applications using this comprehensive guide

Sai S Sriparasa

Building Agentic AI Systems. Create intelligent, autonomous AI agents that can reason, plan, and adapt

Anjanava Biswas, Wrick Talukdar, Matthew R. Scott, Dr. Alex Acero

Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents.Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.

Building AI Agents with LLMs, RAG, and Knowledge Graphs. A practical guide to autonomous and modern AI agents

Salvatore Raieli, Gabriele Iuculano

This AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving.Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together.By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.

Building AI Applications with ChatGPT APIs. Master ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects

Martin Yanev

Combining ChatGPT APIs with Python opens doors to building extraordinary AI applications. By leveraging these APIs, you can focus on the application logic and user experience, while ChatGPT’s robust NLP capabilities handle the intricacies of human-like text understanding and generation.This book is a guide for beginners to master the ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects. These projects offer practical experience in integrating ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt.Throughout this book, you’ll get to grips with performing NLP tasks, building a ChatGPT clone, and creating an AI-driven code bug fixing SaaS application. You’ll also cover speech recognition, text-to-speech functionalities, language translation, and generation of email replies and PowerPoint presentations. This book teaches you how to fine-tune ChatGPT and generate AI art using DALL-E APIs, and then offers insights into selling your apps by integrating ChatGPT API with Stripe. With practical examples available on GitHub, the book gradually progresses from easy to advanced topics, cultivating the expertise required to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.

Building AI Applications with Microsoft Semantic Kernel. Easily integrate generative AI capabilities and copilot experiences into your applications

Lucas A. Meyer

In the fast-paced world of AI, developers are constantly seeking efficient ways to integrate AI capabilities into their apps. Microsoft Semantic Kernel simplifies this process by using the GenAI features from Microsoft and OpenAI.Written by Lucas A. Meyer, a Principal Research Scientist in Microsoft’s AI for Good Lab, this book helps you get hands on with Semantic Kernel. It begins by introducing you to different generative AI services such as GPT-3.5 and GPT-4, demonstrating their integration with Semantic Kernel. You’ll then learn to craft prompt templates for reuse across various AI services and variables. Next, you’ll learn how to add functionality to Semantic Kernel by creating your own plugins. The second part of the book shows you how to combine multiple plugins to execute complex actions, and how to let Semantic Kernel use its own AI to solve complex problems by calling plugins, including the ones made by you. The book concludes by teaching you how to use vector databases to expand the memory of your AI services and how to help AI remember the context of earlier requests. You’ll also be guided through several real-world examples of applications, such as RAG and custom GPT agents.By the end of this book, you'll have gained the knowledge you need to start using Semantic Kernel to add AI capabilities to your applications.