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.
Karim Okasha
Network Automation Cookbook is designed to help system administrators, network engineers, and infrastructure automation engineers to centrally manage switches, routers, and other devices in their organization's network. This book will help you gain hands-on experience in automating enterprise networks and take you through core network automation techniques using the latest version of Ansible and Python. With the help of practical recipes, you'll learn how to build a network infrastructure that can be easily managed and updated as it scales through a large number of devices. You'll also cover topics related to security automation and get to grips with essential techniques to maintain network robustness. As you make progress, the book will show you how to automate networks on public cloud providers such as AWS, Google Cloud Platform, and Azure. Finally, you will get up and running with Ansible 2.9 and discover troubleshooting techniques and network automation best practices. By the end of this book, you'll be able to use Ansible to automate modern network devices and integrate third-party tools such as NAPALM, NetBox, and Batfish easily to build robust network automation solutions.
Yoram Orzach, Deepanshu Khanna
With the increased demand for computer systems and the ever-evolving internet, network security now plays an even bigger role in securing IT infrastructures against attacks. Equipped with the knowledge of how to find vulnerabilities and infiltrate organizations through their networks, you’ll be able to think like a hacker and safeguard your organization’s network and networking devices. Network Protocols for Security Professionals will show you how.This comprehensive guide gradually increases in complexity, taking you from the basics to advanced concepts. Starting with the structure of data network protocols, devices, and breaches, you’ll become familiar with attacking tools and scripts that take advantage of these breaches. Once you’ve covered the basics, you’ll learn about attacks that target networks and network devices. Your learning journey will get more exciting as you perform eavesdropping, learn data analysis, and use behavior analysis for network forensics. As you progress, you’ll develop a thorough understanding of network protocols and how to use methods and tools you learned in the previous parts to attack and protect these protocols.By the end of this network security book, you’ll be well versed in network protocol security and security countermeasures to protect network protocols.
Edward L. Platt
NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use.If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts.By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.
Manpreet Singh Ghotra, Rajdeep Dua
If you're aware of the buzz surrounding the terms such as machine learning, artificial intelligence, or deep learning, you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
V Kishore Ayyadevara
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Tomasz Rymarczyk Krzysztof Król
This publication was prepared as a permanent record of research findings presented by doctoral students and academics representing diverse research backgrounds and schools. The focus is on interdisciplinary solutions, combining engineering, computer science, and mathematical methods with approaches relevant to management science and socio-economic applications. The International Interdisciplinary PhD Workshop (IIPhDW) is a cyclical and international conference, and its primary function is to provide a platform for research presentations, knowledge exchange, and collaboration between young scientists from various disciplines. The 2025 edition featured a particularly strong technical component, encompassing artificial intelligence, computer science, automation and control, robotics and mechatronics, telecommunications, signal processing, as well as mechanical and production engineering. At the same time, the inclusion of topics in economics and management confirmed the workshop's broad scope and its ability to integrate research perspectives relevant to contemporary technological and organizational challenges. A significant group of publications includes works on process and biomedical tomography, as well as image reconstruction methods using machine learning and deep learning, including approaches combining physical models with neural network architectures. Examples include research on image reconstruction in electrical impedance tomography, the integration of tomography with neural networks for industrial process monitoring, and the use of ultrasound tomography in measurement and reconstruction analysis. This theme is further reinforced by works on hybrid tomography systems, process monitoring using mixed reality technology, and applications in the areas of physiological parameter monitoring and non-invasive diagnostics. The second recognizable axis is artificial intelligence in IT and cyberphysical systems, encompassing both the construction of predictive and classification models and their implementation in industrial, medical, and service environments. This trend includes work related to the application of machine learning methods in system and network security, solutions based on LLM agents in project team workflows, processing unstructured data using OCR and language models, and multimodal analysis in intelligent customer service systems. This perspective highlights the contemporary trend of convergence of AI techniques with data engineering, software engineering, and systems integration, which has direct implications for the design of scalable implementation solutions. The third thematic area covers embedded systems, communication, and signal processing, along with elements of computational resource optimization. The monograph includes papers on, among other things, phase shift estimation in noisy environments, pseudorandom sequence generation, acoustic feature detection, and the efficiency of machine learning applications at the network edge in the context of Kubernetes scheduling heuristics. In the monograph, this strand serves a methodological purpose, providing signal analysis tools and computational mechanisms that form the foundation for many AI and measurement system applications. A significant complement to the technical perspective are works in the areas of management and organizational and economic analysis, which address the need to understand the determinants of technology implementation and the conduct of innovative projects. The publications address, among other things, the predictors of success in startup management and the analysis of organizational improvements in public institutions. Their presence strengthens the interdisciplinary nature of the monograph by demonstrating that the effectiveness of engineering solutions depends not only on the quality of algorithms and devices, but also on the organizational, process, and decision-making context. The monograph is intended as a reference for academics and doctoral students, particularly those seeking examples of research that combines theory with application. The collected papers offer a comprehensive overview of research activities typical of early careers in science, from conceptual studies and method analysis, through device and software architecture prototyping, to experiments and evaluation of the effectiveness of proposed solutions. At the same time, the publication allows for the identification of common methodological denominators, such as the growing importance of measurement data, simulation, deep learning, systems integration, and the pursuit of real-time operation in industrial and biomedical environments. The introduction, on the one hand, contextualizes the monograph within the mission of IIPhDW as a workshop supporting researcher development and the internationalization of research. On the other hand, it organizes the chapter topics in the perspective of dominant technological trends and application needs that permeate various fields. Consequently, the monograph can be viewed as a synthetic overview of current research directions for doctoral students and young academics, as well as an inspiration for undertaking work combining artificial intelligence methods, measurement systems, software engineering, and management analyses within modern interdisciplinary projects.
Bethany Griggs
A key technology for building web applications and tooling, Node.js brings JavaScript to the server enabling full-stack development in a common language. This fourth edition of the Node Cookbook is updated with the latest Node.js features and the evolution of the Node.js framework ecosystems.This practical guide will help you to get started with creating, debugging, and deploying your Node.js applications and cover solutions to common problems, along with tips to avoid pitfalls. You'll become familiar with the Node.js development model by learning how to handle files and build simple web applications and then explore established and emerging Node.js web frameworks such as Express.js and Fastify. As you advance, you'll discover techniques for detecting problems in your applications, handling security concerns, and deploying your applications to the cloud. This recipe-based guide will help you to easily navigate through various core topics of server-side web application development with Node.js.By the end of this Node book, you'll be well-versed with core Node.js concepts and have gained the knowledge to start building performant and scalable Node.js applications.
Nowoczesna analiza danych w Excelu. Power Query, Power Pivot i inne narzędzia
George Mount
Organizacje stosują zaawansowane narzędzia i techniki, aby umożliwić podejmowanie decyzji na podstawie danych. Jeszcze do niedawna MS Excel nie był w stanie sprostać wymaganiom analityków, obecnie jest wschodzącą gwiazdą w świecie nowoczesnej analizy danych. Kluczem do sukcesu stało się opanowanie takich narzędzi jak Power Pivot czy Power Query. Dzięki tej praktycznej książce przekonasz się, że w Excelu możesz przeprowadzić dogłębną analizę danych i wyciągnąć z nich cenną wiedzę. Wystarczy, że skorzystasz z najnowszych funkcji i narzędzi Excela. W poradniku pokazano, jak za pomocą Power Query budować przepływy pracy porządkujące dane i jak projektować w skoroszycie relacyjne modele danych przy użyciu Power Pivot. Ponadto odkryjesz nowe możliwości przeprowadzania analiz, w tym dynamiczne funkcje tablicowe i pozyskiwanie wartościowych informacji z wykorzystaniem sztucznej inteligencji. Dowiesz się również, jak za sprawą integracji z Pythonem zautomatyzować analizę danych i budować raporty. W książce: tworzenie procesów porządkujących dane w Excelu za pomocą Power Query relacyjne modele danych i wskaźniki analityczne w Power Pivot korzystanie z dynamicznych funkcji tablicowych zastosowanie AI do identyfikacji wzorców i trendów w danych integracja funkcjonalności Pythona z Excelem Książka jest łatwa w odbiorze, praktyczna i pomocna dla analityków, a także zwykłych użytkowników! Meghan Finley, autorka i redaktorka tekstów technicznych