Wydawca: Packt Publishing

Deep Reinforcement Learning Hands-On. A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF - Third Edition

Maxim Lapan

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the field, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion*Email sign-up and proof of purchase required

Deep Reinforcement Learning Hands-On. Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more - Second Edition

Maxim Lapan

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

Deep Reinforcement Learning Hands-On. Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Oleg Vasilev, Maxim Lapan, Martijn van Otterlo,...

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

Deep Reinforcement Learning with Python. Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow - Second Edition

Sudharsan Ravichandiran

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

DeepSeek in Practice. From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM

Andy Peng, Alex Strick van Linschoten, Duarte...

Learn how to build, fine-tune, and deploy AI systems using DeepSeek, one of the most influential open-source large language models available today. This book guides you through real-world DeepSeek applications—from understanding its core architecture and training foundations to developing reasoning agents and deploying production-ready systems.Starting with a concise synthesis of DeepSeek's research, breakthroughs, and open-source philosophy, you’ll progress to hands-on projects including prompt engineering, workflow design, and rationale distillation. Through detailed case studies—ranging from document understanding to legal clause analysis—you’ll see how to use DeepSeek in high-value GenAI scenarios.You’ll also learn to build sophisticated agent workflows and prepare data for fine-tuning. By the end of the book, you’ll have the skills to integrate DeepSeek into local deployments, cloud CI/CD pipelines, and custom LLMOps environments.Written by experts with deep knowledge of open-source LLMs and deployment ecosystems, this book is your comprehensive guide to DeepSeek’s capabilities and implementation.

Defending APIs. Uncover advanced defense techniques to craft secure application programming interfaces

Colin Domoney, Chris Wysopal, Isabelle Mauny

Along with the exponential growth of API adoption comes a rise in security concerns about their implementation and inherent vulnerabilities. For those seeking comprehensive insights into building, deploying, and managing APIs as the first line of cyber defense, this book offers invaluable guidance. Written by a seasoned DevSecOps expert, Defending APIs addresses the imperative task of API security with innovative approaches and techniques designed to combat API-specific safety challenges.The initial chapters are dedicated to API building blocks, hacking APIs by exploiting vulnerabilities, and case studies of recent breaches, while the subsequent sections of the book focus on building the skills necessary for securing APIs in real-world scenarios.Guided by clear step-by-step instructions, you’ll explore offensive techniques for testing vulnerabilities, attacking, and exploiting APIs. Transitioning to defensive techniques, the book equips you with effective methods to guard against common attacks. There are plenty of case studies peppered throughout the book to help you apply the techniques you’re learning in practice, complemented by in-depth insights and a wealth of best practices for building better APIs from the ground up.By the end of this book, you’ll have the expertise to develop secure APIs and test them against various cyber threats targeting APIs.

Defensive Security with Kali Purple. Cybersecurity strategies using ELK Stack and Kali Linux

Karl Lane

Defensive Security with Kali Purple combines red team tools from the Kali Linux OS and blue team tools commonly found within a security operations center (SOC) for an all-in-one approach to cybersecurity. This book takes you from an overview of today's cybersecurity services and their evolution to building a solid understanding of how Kali Purple can enhance training and support proof-of-concept scenarios for your technicians and analysts.After getting to grips with the basics, you’ll learn how to develop a cyber defense system for Small Office Home Office (SOHO ) services. This is demonstrated through the installation and configuration of supporting tools such as virtual machines, the Java SDK, Elastic, and related software. You’ll then explore Kali Purple’s compatibility with the Malcolm suite of tools, including Arkime, CyberChef, Suricata, and Zeek. As you progress, the book introduces advanced features, such as security incident response with StrangeBee’s Cortex and TheHive and threat and intelligence feeds. Finally, you’ll delve into digital forensics and explore tools for social engineering and exploit development.By the end of this book, you’ll have a clear and practical understanding of how this powerful suite of tools can be implemented in real-world scenarios.

Dell VxRail System Design and Best Practices. A complete guide to VxRail appliance design and best practices

Victor Wu

Virtualized systems are well established now, and their disparate components can be found bundled together in hyper-converged infrastructures, such as VxRail from Dell EMC. Dell VxRail System Design and Best Practices will take you, as a system architect or administrator, through the process of designing and protecting VxRail systems.While this book assumes a certain level of knowledge of VMware, vSphere 7.x, and vCenter Server, you’ll get a thorough overview of VxRail's components, features, and architecture, as well as a breakdown of the benefits of this hyper-converged system. This guide will give you an in-depth understanding of VxRail, as well as plenty of practical examples and self-assessment questions along the way to help you plan and design every core component of a VxRail system – from vSAN storage policies to cluster expansion. It's no good having a great system if you lose everything when it breaks, so you'll spend some time examining advanced recovery options, such as VMware Site Recovery Manager and Veeam Backup and Replication.By the end of this book, you will have got to grips with Dell’s hyper-converged VxRail offering, taking your virtualization proficiency to the next level.