Видавець: 16

Macedonia. W sercu Bałkanów. Wydanie 1

Robert Sendek, Magdalena Dobrzańska-Bzowska

Macedonia, położona niemal dokładnie w środku Półwyspu Bałkańskiego, rzadko bywa głównym celem turystów z Polski. Tymczasem miłośnicy kultury ludowej i ciekawych zabytków, poszukiwacze urokliwych zakątków, a zwłaszcza górołazi zdecydowanie powinni zwrócić na ten kraj należną mu uwagę. Stołeczne Skopje oczarowuje egzotyczną starówką, jednocześnie zdumiewając rozmachem i kiczem pełnego monumentalnych pomników nowego centrum, zaś historyczne miasta i górskie wsie kryją niezliczone zabytki, ze zmurszałymi od starości cerkwiami na czele. Jeśli dodamy do tego barwną kulturę Macedończyków, z żywiołowymi tańcami, pięknymi pieśniami i znakomitą kuchnią regionalną, piękne jeziora oraz rozległe, dzikie i wyniosłe góry zajmujące większą część powierzchni kraju, otrzymamy jedyny w swoim rodzaju bałkański kalejdoskop, po spojrzeniu w który zapragniemy spakować plecak i ruszyć na południe. Lubisz podróżować samodzielnie? Chciałbyś poznać historię, kulturę i przyrodnicze atrakcje odwiedzanych krajów, regionów i miast? Seria Bezdroża Classic powstała z myślą o Tobie! Ten przewodnik pomoże Ci zaplanować wyjazd, stanie się Twoim wiernym towarzyszem podróży i zapewni pasjonującą lekturę także po powrocie!   Robert Sendek – slawista, którego główną fascynacją pozostają Bałkany jako niepowtarzalny tygiel narodów, kultur, tradycji i języków. Są dla niego najważniejszym terenem wypraw i częstych wizyt, przemierzył je wzdłuż i wszerz, od Podgoricy do Bukaresztu i od Zagrzebia do Salonik. Szczególną wagę przywiązuje do wędrówek po Bułgarii, najpiękniejszym kraju na całym półwyspie, oraz po Macedonii, będącej prawdziwym sercem Bałkanów, wręcz ich kwintesencją, ze wszystkimi ich problemami, radościami i ambicjami. Obronił doktorat poświęcony bułgarskiej i macedońskiej frazeologii w kontekście bałkańskim. Pracuje w Katedrze Filologii Bułgarskiej i Macedońskiej UJ. Jest autorem przewodnika Bułgaria. Pejzaż słońcem pisany. Magdalena Dobrzańska-Bzowska – historyk, absolwentka Uniwersytetu Jagiellońskiego. Pasja podróżnicza sprawia, że dłuższy pobyt w jednym miejscu jest dla niej trudny do zniesienia, ciągle zatem wyjeżdża, by zwiedzać kolejne krainy – te całkiem zapomniane, biedne regiony w naszej części Europy, jak i te w Europie Zachodniej – znane wszystkim turystom. Niejeden raz wyruszała też poza nasz kontynent, zafascynowana bogactwem kultur Bliskiego Wschodu. Na Bałkanach, w tym w Macedonii, spędziła wiele tygodni, przemierzając je wzdłuż i wszerz, głównie autostopem – w ten sposób poznając nie tylko zabytki, ale i mieszkańców tej części Europy.

MacHassan Ah

Talbot Mundy

This was not until they left their right front and went to a narrow, single-track road that lay under the wall and was securely guarded. Suddenly there appeared an awkward Asian gentleman, who once was the image of a Biblical shepherd.

Machine Learning & Python for Absolute Beginners. A Hands-On Guide to Python Programming and Machine Learning from Scratch

Oliver Theobald

Starting with Python syntax and data types, this guide builds toward implementing key machine learning models. Learn about loops, functions, OOP, and data cleaning, then transition into algorithms like regression, KNN, and neural networks. A final section walks you through model optimization and building projects in Python.The book is split into two major sections—foundational Python programming and introductory machine learning. Readers are guided through essential concepts such as data types, variables, control flow, object-oriented programming, and using libraries like pandas for data manipulation.In the machine learning section, topics like model selection, supervised vs unsupervised learning, bias-variance, and common algorithms are demystified with practical coding examples. It’s a structured, clear roadmap to mastering both programming and applied ML from zero knowledge.

Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning

Giuseppe Bonaccorso

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem

Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition

Giuseppe Bonaccorso

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.

Machine Learning and AI for Absolute Beginners. The Ultimate Guide to AI and Machine Learning for Newcomers

Oliver Theobald

This book is an ideal starting point for anyone interested in Artificial Intelligence and Machine Learning. It begins with the foundational principles of AI, offering a deep dive into its history, building blocks, and the stages of development. Readers will explore key AI concepts and gradually transition to practical applications, starting with machine learning algorithms such as linear regression and k-nearest neighbors. Through step-by-step Python tutorials, the book helps readers build and implement models with hands-on experience.As the book progresses, readers will dive into advanced AI topics like deep learning, natural language processing (NLP), and generative AI. Topics such as recommender systems and computer vision demonstrate the real-world applications of AI technologies. Ethical considerations and privacy concerns are also addressed, providing insight into the societal impact of these technologies.By the end of the book, readers will have a solid understanding of both the theory and practice of AI and Machine Learning. The final chapters provide resources for continued learning, ensuring that readers can continue to grow their AI expertise beyond the book.

Machine Learning and Generative AI for Marketing. Take your data-driven marketing strategies to the next level using Python

Yoon Hyup Hwang, Nicholas C. Burtch

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience.This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales.Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standardsBy the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.

Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems

Gregory Keys, David Whiting

H2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments.Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You’ll start by exploring H2O’s in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You’ll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You’ll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you’ll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities.By the end of this book, you’ll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs.