After the shock of the 1920 Treaty of Trianon, which Hungarians perceived as an unfair dictate, the leaders of the country found it imperative to change Hungary's international image in a way that would help the revision of the post-World War I settlement. The monograph examines the development of interwar Hungarian cultural diplomacy in three areas: universities, the tourist industry, and the media-primarily motion pictures and radio production. It is a story of the Hungarian elites' high hopes and deep-seated anxieties about the country's place in a Europe newly reconstructed after World War I, and how these elites perceived and misperceived themselves, their surroundings, and their own ability to affect the country's fate. The defeat in the Great War was crushing, but it was also stimulating, as Nagy documents in his examination of foreign language journals, tourism, radio, and other tools of cultural diplomacy. The mobilization of diverse cultural and intellectual resources, the author argues, helped establish Hungary's legitimacy in the international arena, contributed to the modernization of the country, and established a set of enduring national images. Though the study is rooted in Hungary, it explores the dynamic and contingent relationship between identity construction and transnational cultural and political currents in East-Central European nations in the interwar period.

Gritty crime thriller, from director Stuart St Paul, about a group of Russian criminals operating a sex trade in the heart of Northern England. Led by the ruthless Cristi (Danny Midwinter), the gang kidnaps and traffics a group of Eastern Europeans to Leeds, forcing the men into illegal fighting and the women into the sex industry. When they capture local businessman Gabe Taylor (Billy Murray)'s daughter, however, things turn ugly, and it's a race against time to get her back alive in time for her wedding.

This quick guide to regular expressions is a condensed code and syntax reference for an important programming technique. It demonstrates regex syntax in a well-organized format that can be used as a handy reference, showing you how to execute regexes in many languages, including JavaScript, Python, Java, and C#. The Regex Quick Syntax Reference features short, focused code examples that show you how to use regular expressions to validate user input, split strings, parse input, and match patterns. Utilizing regular expressions to deal with search/replace and filtering data for backend coding is also covered. You won't find any bloated samples, drawn out history lessons, or witty stories in this book. What you will find is a language reference that is concise and highly accessible. The book is packed with useful information and is a must-have for any programmer. What You Will Learn Formulate an expression Work with arbitrary char classes, disjunctions, and operator precedence Execute regular expressions and visualize using finite state machines Deal with modifiers, including greedy and lazy loops Handle substring extraction from regex using Perl 6 capture groups, capture substrings, and reuse substrings Who This Book Is For If you have dealt with at least one programming language, chances are you know enough to understand regular expressions, and the examples in this book will help you develop proficiency.

With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities Key Features Learn about AI and ML algorithms from the perspective of a seasoned data scientist Get practical experience in ML algorithms, such as regression, tree algorithms, clustering, and more Design neural networks that emulate the human brain Book DescriptionYou already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You'll then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you'll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you'll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you'll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models. What you will learn Create your first AI game in Python with the minmax algorithm Implement regression techniques to simplify real-world data Experiment with classification techniques to label real-world data Perform predictive analysis in Python using decision trees and random forests Use clustering algorithms to group data without manual support Learn how to use neural networks to process and classify labeled images Who this book is forThe Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high school-level mathematics and at least one programming language, preferably Python. Although this is a beginner's book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.

Create AI applications in Python and lay the foundations for your career in data science Key Features Practical examples that explain key machine learning algorithms Explore neural networks in detail with interesting examples Master core AI concepts with engaging activities Book DescriptionMachine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learn Understand the importance, principles, and fields of AI Implement basic artificial intelligence concepts with Python Apply regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out clustering using the k-means and mean shift algorithms Understand the fundamentals of deep learning via practical examples Who this book is forArtificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it's recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

The Semantic Web is a new area of research and development in the field of computer science that aims to make it easier for computers to process the huge amount of information on the web, and indeed other large databases, by enabling them not only to read, but also to understand the information. Based on successful courses taught by the authors, and liberally sprinkled with examples and exercises, this comprehensive textbook describes not only the theoretical issues underlying the Semantic Web, but also algorithms, optimisation ideas and implementation details. The book will therefore be valuable to practitioners as well as students, indeed to anyone who is interested in Internet technology, knowledge engineering or description logics. Supplementary materials available online include the source code of program examples and solutions to selected exercises.