Programowanie
Nicolas Leiva, Michael Kashin
Go’s built-in first-class concurrency mechanisms make it an ideal choice for long-lived low-bandwidth I/O operations, which are typical requirements of network automation and network operations applications.This book provides a quick overview of Go and hands-on examples within it to help you become proficient with Go for network automation. It’s a practical guide that will teach you how to automate common network operations and build systems using Go.The first part takes you through a general overview, use cases, strengths, and inherent weaknesses of Go to prepare you for a deeper dive into network automation, which is heavily reliant on understanding this programming language. You’ll explore the common network automation areas and challenges, what language features you can use in each of those areas, and the common software tools and packages. To help deepen your understanding, you’ll also work through real-world network automation problems and apply hands-on solutions to them.By the end of this book, you’ll be well-versed with Go and have a solid grasp on network automation.
Claus Töpke
Network programming and automation, unlike traditional networking, is a modern-day skill that helps in configuring, managing, and operating networks and network devices. This book will guide you with important information, helping you set up and start working with network programming and automation.With Network Programming and Automation Essentials, you’ll learn the basics of networking in brief. You’ll explore the network programming and automation ecosystem, learn about the leading programmable interfaces, and go through the protocols, tools, techniques, and technologies associated with network programming. You’ll also master network automation using Python and Go with hands-on labs and real network emulation in this comprehensive guide.By the end of this book, you’ll be well equipped to program and automate networks efficiently.
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.
Next.js Quick Start Guide. Server-side rendering done right
Kirill Konshin
Next.js is a powerful addition to the ever-growing and dynamic JavaScript world. Built on top of React, Webpack, and Babel, it is a minimalistic framework for server-rendered universal JavaScript applications. This book will show you the best practices for building sites using Next. js, enabling you to build SEO-friendly and superfast websites.This book will guide you from building a simple single page app to a scalable and reliable client-server infrastructure. You will explore code sharing between client and server, universal modules, and server-side rendering.The book will take you through the core Next.js concepts that everyone is talking about – hot reloading, code splitting, routing, server rendering, transpilation, CSS isolation, and more. You will learn ways of implementing them in order to create your own universal JavaScript application. You will walk through the building and deployment stages of your applications with the JSON API,customizing the confguration, error handling,data fetching, deploying to production, and authentication.