Python
W kategorii Python zostały zebrane podręczniki poruszające tematykę programowania z zastosowaniem praktycznie niezależnego sprzętowo, dostępnego na licencji Open Source języka. Książki przedstawią Wam wszechstronności i elastyczności Pythona a także różne typy tworzenia kodu poprzez programowanie strukturalne, obiektowe czy funkcjonalne.
Nauczycie się tworzyć aplikacje sieciowe o dowolnym przeznaczeniu, komunikujące się z systemami operacyjnymi, lub korzystające z baz danych. Techniki analizy składni, przetwarzanie tekstu czy rozłożenie obciążenia programu na wiele wątków i procesów przestanie być problematyczne.
Hands-On Neural Networks. Learn how to build and train your first neural network model using Python
Leonardo De Marchi, Laura Mitchell
Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics.Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks.By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.
Paolo Galeone
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Iaroslav Omelianenko
Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems.You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones.By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.
Shruti Jadon, Ankush Garg
One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples.Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence.By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.
Furqan Khan
With the current technological and infrastructural shift, penetration testing is no longer a process-oriented activity. Modern-day penetration testing demands lots of automation and innovation; the only language that dominates all its peers is Python. Given the huge number of tools written in Python, and its popularity in the penetration testing space, this language has always been the first choice for penetration testers.Hands-On Penetration Testing with Python walks you through advanced Python programming constructs. Once you are familiar with the core concepts, you’ll explore the advanced uses of Python in the domain of penetration testing and optimization. You’ll then move on to understanding how Python, data science, and the cybersecurity ecosystem communicate with one another. In the concluding chapters, you’ll study exploit development, reverse engineering, and cybersecurity use cases that can be automated with Python.By the end of this book, you’ll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits.
Alvaro Fuentes
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
Anubhav Singh, Sayak Paul
When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python.Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages.By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices.
Aman Kedia, Mayank Rasu
Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding.This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own.By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
Hands-On Q-Learning with Python. Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
Nazia Habib
Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning.By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.
Hands-On Reactive Programming with Python. Event-driven development unraveled with RxPY
Romain Picard
Reactive programming is central to many concurrent systems, but it’s famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various conceptsThis hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application.By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices.
Micheal Lanham
With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python.Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games.By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.
Sudharsan Ravichandiran
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Harihara Subramanian J, Anupama Murali, Pethuru Raj
This book deals with the Representational State Transfer (REST) paradigm, which is an architectural style that allows networked devices to communicate with each other over the internet. With the help of this book, you’ll explore the concepts of service-oriented architecture (SOA), event-driven architecture (EDA), and resource-oriented architecture (ROA). This book covers why there is an insistence for high-quality APIs toward enterprise integration.It also covers how to optimize and explore endpoints for microservices with API gateways and touches upon integrated platforms and Hubs for RESTful APIs. You’ll also understand how application delivery and deployments can be simplified and streamlined in the REST world. The book will help you dig deeper into the distinct contributions of RESTful services for IoT analytics and applications.Besides detailing the API design and development aspects, this book will assist you in designing and developing production-ready, testable, sustainable, and enterprise-grade APIs. By the end of the book, you’ll be empowered with all that you need to create highly flexible APIs for next-generation RESTful services and applications.
Gastón C. Hillar
Python is the language of choice for millions of developers worldwide that builds great web services in RESTful architecture. This second edition of Hands-On RESTful Python Web Services will cover the best tools you can use to build engaging web services.This book shows you how to develop RESTful APIs using the most popular Python frameworks and all the necessary stacks with Python, combined with related libraries and tools. You’ll learn to incorporate all new features of Python 3.7, Flask 1.0.2, Django 2.1, Tornado 5.1, and also a new framework, Pyramid. As you advance through the chapters, you will get to grips with each of these frameworks to build various web services, and be shown use cases and best practices covering when to use a particular framework.You’ll then successfully develop RESTful APIs with all frameworks and understand how each framework processes HTTP requests and routes URLs. You’ll also discover best practices for validation, serialization, and deserialization. In the concluding chapters, you will take advantage of specific features available in certain frameworks such as integrated ORMs, built-in authorization and authentication, and work with asynchronous code. At the end of each framework, you will write tests for RESTful APIs and improve code coverage.By the end of the book, you will have gained a deep understanding of the stacks needed to build RESTful web services.
Giuseppe Ciaburro
Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Nimesh Verma, Brian Allbee
Software Engineering is about more than just writing code—it includes a host of soft skills that apply to almost any development effort, no matter what the language, development methodology, or scope of the project. Being a senior developer all but requires awareness of how those skills, along with their expected technical counterparts, mesh together through a project's life cycle. This book walks you through that discovery by going over the entire life cycle of a multi-tier system and its related software projects. You'll see what happens before any development takes place, and what impact the decisions and designs made at each step have on the development process. The development of the entire project, over the course of several iterations based on real-world Agile iterations, will be executed, sometimes starting from nothing, in one of the fastest growing languages in the world—Python. Application of practices in Python will be laid out, along with a number of Python-specific capabilities that are often overlooked. Finally, the book will implement a high-performance computing solution, from first principles through complete foundation.
Giuseppe Bonaccorso
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
Luca Venturi, Krishtof Korda
The visual perception capabilities of a self-driving car are powered by computer vision. The work relating to self-driving cars can be broadly classified into three components - robotics, computer vision, and machine learning. This book provides existing computer vision engineers and developers with the unique opportunity to be associated with this booming field.You will learn about computer vision, deep learning, and depth perception applied to driverless cars. The book provides a structured and thorough introduction, as making a real self-driving car is a huge cross-functional effort. As you progress, you will cover relevant cases with working code, before going on to understand how to use OpenCV, TensorFlow and Keras to analyze video streaming from car cameras. Later, you will learn how to interpret and make the most of lidars (light detection and ranging) to identify obstacles and localize your position. You’ll even be able to tackle core challenges in self-driving cars such as finding lanes, detecting pedestrian and crossing lights, performing semantic segmentation, and writing a PID controller.By the end of this book, you’ll be equipped with the skills you need to write code for a self-driving car running in a driverless car simulator, and be able to tackle various challenges faced by autonomous car engineers.
Harpreet Singh, Himanshu Sharma
Metasploit has been a crucial security tool for many years. However, there are only a few modules that Metasploit has made available to the public for pentesting web applications. In this book, you'll explore another aspect of the framework – web applications – which is not commonly used. You'll also discover how Metasploit, when used with its inbuilt GUI, simplifies web application penetration testing. The book starts by focusing on the Metasploit setup, along with covering the life cycle of the penetration testing process. Then, you will explore Metasploit terminology and the web GUI, which is available in the Metasploit Community Edition. Next, the book will take you through pentesting popular content management systems such as Drupal, WordPress, and Joomla, which will also include studying the latest CVEs and understanding the root cause of vulnerability in detail. Later, you'll gain insights into the vulnerability assessment and exploitation of technological platforms such as JBoss, Jenkins, and Tomcat. Finally, you'll learn how to fuzz web applications to find logical security vulnerabilities using third-party tools. By the end of this book, you'll have a solid understanding of how to exploit and validate vulnerabilities by working with various tools and techniques.
Anish Chapagain
Web scraping is an essential technique used in many organizations to gather valuable data from web pages. This book will enable you to delve into web scraping techniques and methodologies.The book will introduce you to the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. You'll use powerful libraries from the Python ecosystem such as Scrapy, lxml, pyquery, and bs4 to carry out web scraping operations. You will then get up to speed with simple to intermediate scraping operations such as identifying information from web pages and using patterns or attributes to retrieve information. This book adopts a practical approach to web scraping concepts and tools, guiding you through a series of use cases and showing you how to use the best tools and techniques to efficiently scrape web pages. You'll even cover the use of other popular web scraping tools, such as Selenium, Regex, and web-based APIs.By the end of this book, you will have learned how to efficiently scrape the web using different techniques with Python and other popular tools.
Hello World! Przygoda z programowaniem dla dzieci i absolutnie początkujących. Wydanie II
Warren Sande, Carter Sande
Nauka programowania to świetny pomysł, ale musimy Cię ostrzec: programowanie wciąga! Jeśli nauczysz się porozumiewać z komputerem w jego języku, szybko zorientujesz się, że napisanie własnej gry, skryptu do złożonych obliczeń czy funkcjonalnej aplikacji do codziennego użytku daje wielką satysfakcję i sprawia mnóstwo radości. Co więcej, programowania można nauczyć się niemal w każdym wieku. Wystarczy odrobina cierpliwości i konsekwencji! Trzymasz w ręce chyba najlepszy podręcznik do nauki programowania dla osób absolutnie początkujących — nawet takich, które komputera używają wyłącznie do przeglądania stron i obsługi poczty. W sposób szczególny nadaje się on dla dzieci, ale skorzystają z niego również dorośli, którzy chcą poznać podstawy programowania. Jasno i klarownie przedstawiono tu wszystkie niezbędne informacje, a liczne (bardzo zabawne) przykłady pozwalają na głębsze zrozumienie prezentowanych treści. Nauka odbywa się w języku Python. Jest to język łatwy w nauce, a przy tym bardzo popularny i wciąż rozwijany. Co więcej, przyswojenie sobie Pythona pozwala na szybką naukę innych języków! W tym podręczniku znajdziesz informacje m.in. o tym: jak przygotować się do pracy: zainstalować Pythona i napisać pierwsze linie kodu czym są i do czego służą zmienne, typy danych, operatory czym jest GUI i jak go napisać jakie są rodzaje pętli, co to są funkcje i na czym polega obsługa zdarzeń jak przygotować symulacje komputerowe Witaj w niezwykłym świecie programowania! Warren Sande — jest inżynierem systemów elektronicznych. Na co dzień korzysta z Pythona zarówno do nauczania programowania, jak i do pisania w pełni profesjonalnych skryptów. Carter Sande — jest uczniem o wielkiej pasji do technologii. Troszczy się o szkolną sieć informatyczną, chętnie pomaga kolegom w rozwiązywaniu problemów z komputerami, a ponadto uwielbia jeździć na rowerze i grać w stare gry wideo.
Pietro Libro, Artem Lajko
This book covers actionable GitOps practices for automated, secure Kubernetes deployments with industry-tested scenarios. You’ll be able to leverage GitOps to tackle cloud-native software delivery challenges, such as preventing data drifts between systems and Git repositories, and ensuring rapid, error-free deployments.The book introduces GitOps core concepts and principles and then delves into integrating version control and GitOps tools such as Argo CD, Flux CD, Helm, and Kustomize with Kubernetes. You’ll learn about scaling GitOps across multiple clusters, architectural designs for efficient cloud-native operations, and the cultural transformation needed to support GitOps adoption within an organization. As you progress, you’ll be able to automate infrastructure and implement CI/CD processes on OpenShift, MS Azure, and AWS platforms using GitOps, Terraform, OpenTofu, and Argo CD. You’ll also explore examples and best practices for integrating observability, enhancing security, managing financial operations (FinOps), and future trends such as AI and sustainability in GitOps.By the end of this book, you’ll have gained the skills you need to apply GitOps strategies for robust Kubernetes and cloud deployments, thereby boosting your productivity and efficiency.
Implementowanie Czystej Architektury w Pythonie
Sebastian Buczyński
Zaawansowane programowanie zaczyna się tam, gdzie kończy się framework Praca programisty wydaje się dziś znacznie prostsza niż kilkanaście lat temu. Wydaje się taka, ponieważ ma on dostęp do niezliczonych bibliotek przypisanych do języków programowania. Gdy pojawia się problem, sięga do biblioteki ― i po sprawie. Problem rozwiązany, zgadza się? I tak, i nie. Owszem, w wypadku prostych projektów wystarczy bazowa znajomość zasad programowania, podstawowa znajomość danego języka i wiedza na temat tego, co zawiera konkretna biblioteka. Tak jednak działa to jedynie przy nieskomplikowanych aplikacjach. Bez wątpienia dziś łatwiej zacząć programować i szybciej można uzyskać mierzalne efekty, ale... ...prawdziwe programowanie zaczyna się poziom wyżej. Na etapie większych projektów. Bo duże systemy, niezależnie od języka, w jakim zostały napisane, zawsze są trudne ― zarówno w rozwijaniu, jak i w utrzymaniu. Książka, którą trzymasz w ręku, została napisana ze świadomością tej programistycznej prawdy. Adresowana do średnio zaawansowanych programistów zajmujących się rozwojem aplikacji internetowych, stanowi kompletny przewodnik po implementacji czystej architektury. Znajdziesz tu także opisy wielu technik, które pomogą Ci zapanować nad projektami rozwijanymi od dłuższego czasu, takich jak strategia testowania czy modularyzacja. Dzięki ich opanowaniu będzie Ci o wiele łatwiej dbać o poprawność funkcjonowania systemów, nad którymi sprawujesz programistyczną pieczę.
Infrastructure as Code with Pulumi. Streamlining Cloud Deployments Using Code
Adora Nwodo
Infrastructure Automation with Pulumi is your ultimate guide to mastering infrastructure as code across multi-cloud environments. This comprehensive resource walks you through setting up Pulumi, deploying across major cloud providers, and confidently scaling complex architectures. Starting with the fundamentals of infrastructure as code, you'll set up Pulumi, learn its core concepts like resources, stacks, and state management, and build your first infrastructure projects. As you progress, you'll explore advanced techniques for deploying on AWS, Azure, Google Cloud, and Kubernetes. You'll also integrate Pulumi into CI/CD pipelines for continuous deployment and automate cloud infrastructure management. You'll dive deep into Pulumi's provider ecosystem, tackle real-world challenges like multi-region, multi-cloud, and hybrid deployments, and ensure compliance using Policy as Code techniques. With practical examples, real-world scenarios, and hands-on exercises, you'll gain the skills to confidently build scalable, secure, and efficient cloud infrastructure using Pulumi. By the end of this book, you'll have mastered Pulumi's advanced capabilities, applied best practices for maintainable and testable infrastructure code, and be ready to migrate existing projects from other IaC tools to Pulumi seamlessly.