Macedonia 1: Giaurowie Macedonii. Opis magii i religii pasterzy z Porecza na tle zbiorowego życia ich wsi

Józef Obrębski

I część Macedonii Józefa Obrębskiego zawiera materiały do monografii Giaurowie Macedonii. Opis magii i religii pasterzy z Porecza na tle zbiorowego życia ich wsi. Książka ta, przygotowana do druku, uległa zniszczeniu w czasie powstania warszawskiego. Redaktorzy tomu opracowali zachowane w spuściźnie archiwalnej uczonego rozdziały, fragmenty i warianty tekstu, a także macedońskie notatki terenowe, które zaopatrzyli w polski przekład. Publikowane ponad 80 lat po badaniach terenowych Obrębskiego na macedońskim Poreczu jego pisma i materiały okazują się antropologicznym odkryciem. Odrzuceni na krańce cywilizacji europejskiej, od niedawna [...] włączeni w terytorialny obszar narodowej wspólnoty jugosłowiańskiej, po ostatnie nieomal lata wiodąc żywot swój według wzorów zostawionych im przez ich dziadów i pradziadów, Poreczanie - z całą swoją archaiczną kulturą ludową i tradycyjnym tokiem życia - znajdowali się poza nawiasem współczesnej historii, poza nurtem wielkich przemian i wydarzeń dziejowych współczesności.

Macedonia 2: Czarownictwo Porecza Macedońskiego. Mit i rzeczywistość u Słowian Południowych

Józef Obrębski

W skład II części Macedonii wchodzą monografie Józefa Obrębskiego Czarownictwo Porecza Macedońskiego oraz Mit i rzeczywistość u Słowian Południowych, zrekonstruowane z materiałów archiwalnych. Znajduje się tu również bogaty korpus źródeł ustnych (w oryginalnej wersji macedońskiej i polskim przekładzie), w tym rzadkie teksty epickie i liryczne, a także studium Struktura społeczna i rytuał we wsi macedońskiej - jedyna praca dotycząca kultury Porecza, jaką Obrębski napisał po drugiej wojnie światowej, adresując ją do amerykańskiej publiczności antropologicznej.

Macedonia. W sercu Bałkanów. Wydanie 1

Magdalena Dobrzańska-Bzowska, Robert Sendek

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 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 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.

Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python

Dario Radečić

The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets.By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.

Machine Learning: End-to-End guide for Java developers. Data Analysis, Machine Learning, and Neural Networks simplified

Boštjan Kaluža, Krishna Choppella, Uday Kamath

Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books:[*]Java for Data Science[*]Machine Learning in Java [*]Mastering Java Machine LearningOn completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.

Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition

Andrew P. McMahon, Adi Polak

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized model factory for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.

Machine Learning Engineering on AWS. Build, scale, and secure machine learning systems and MLOps pipelines in production

Joshua Arvin Lat

There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production.This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you’ll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You’ll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS.By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.

Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow

Natu Lauchande

MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments.This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you’ll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.

Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples

Andrew McMahon

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

Machine Learning for Algorithmic Trading. Predictive models to extract signals from market and alternative data for systematic trading strategies with Python - Second Edition

Stefan Jansen

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

Machine Learning for Cybersecurity Cookbook. Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

Emmanuel Tsukerman

Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers.You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.