Verleger: M-t
Jo Nesboo
Lata70.,skorumpowane miasto pełne przestępców. Macbeth, jeden z niewielu nieprzekupnych policjantów, jest dowódcą jednostki specjalnej. Po niebezpiecznej akcji przeciwko handlarzom narkotyków zaczyna piąć się po szczeblach kariery. Wspiera go Lady, piękna i wpływowa właścicielka kasyna. Mężczyzna w młodości zmagał się z problemem narkotykowym, ale uzależnienie od władzy okazuje się znacznie bardziej niebezpieczne
Jo Nesboo
Lata70.,skorumpowane miasto pełne przestępców. Macbeth, jeden z niewielu nieprzekupnych policjantów, jest dowódcą jednostki specjalnej. Po niebezpiecznej akcji przeciwko handlarzom narkotyków zaczyna piąć się po szczeblach kariery. Wspiera go Lady, piękna i wpływowa właścicielka kasyna. Mężczyzna w młodości zmagał się z problemem narkotykowym, ale uzależnienie od władzy okazuje się znacznie bardziej niebezpieczne
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
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.
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.
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.
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
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.
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.
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.
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.
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.
Uday Kamath, Krishna Choppella
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.
Andrew P. McMahon
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.
Joshua Arvin Lat
Modern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required