Електронні книги
37033
Eлектронна книга

Quo vadis? Henryka Sienkiewicza. Od legendy do arcydzieła

Teresa Świętosławska

W studwudziestolecie edycji dzieła, stusiedemdziesięciolecie urodzin pisarza i stulecie jego śmierci, w Roku Henryka Sienkiewicza, gdy powieść wybrana została w ogólnopolskim plebiscycie jako lektura Narodowego Czytania, potrzeba nowego spojrzenia na „najsławniejszą powieść polską” (jak nazwał ją Julian Krzyżanowski), przełożoną na ponad 50 języków, wielokrotnie ekranizowaną i adaptowaną, wydaje się oczywista. Autorka, przybliżając legendę genezy powieści i obszar jej recepcji, a także złożoną problematykę relacji dwóch kultur w rycie przejścia (Rzymu antycznego i rodzącej się tradycji chrześcijańskiej), rozważa (i rozstrzyga), czy można (i dlaczego) nadać Quo vadis? status eposowego arcydzieła. W studium o tytułowym dziele autorka podkreśla również, że nie tylko świat antyku współtworzył europejską kulturę, lecz także – in statu nascendi – krąg Biblii, a spłycanie wpływu zwłaszcza Nowego Testamentu na powieść Sienkiewicza zuboża jej interpretacje. Poprzez kontakt z Quo vadis? Czytelnik doświadczy otwarcia perspektywy na nowy, ważny dla dzieła, obszar tematyczny – problematykę chrześcijańską i związane z nią wartości. 

37034
Eлектронна книга

Quo vadis, Rosjo? Spojrzenie ambasadora Niemiec

Ruediger von Fritsch

Wspomnienia i analizy Rüdigera von Fritscha, ambasadora Niemiec w Moskwie w latach 2014–2019 (wcześniej, w latach 2010–2014, w Warszawie) dotyczą historii Rosji, jej obecnej polityki wewnętrznej oraz zagranicznej, stosunków z Niemcami i Unią Europejską, a także dzisiejszego postrzegania przez Rosję swojej roli w świecie. Autor, który rozpoczął misję w Moskwie w roku aneksji Krymu, w sposób jednoznaczny broni stanowiska demokratycznej wspólnoty międzynarodowej i polityki sankcji wobec Rosji. Piętnuje nieakceptowalne działania Rosji – wspieranie separatystów na wschodzie Ukrainy, zamachy i morderstwa (Litwinienko, Skripal, Nawalny), zestrzelenie samolotu holenderskiego, ataki hakerskie i ingerowanie w procesy wyborcze w innych krajach – ale jednocześnie przekonująco argumentuje na rzecz utrzymania dialogu z rządem Rosji i rozwijania stosunków kulturalnych, naukowych i gospodarczych, podkreślając korzyści wynikające z podtrzymywania takich relacji. Wskazuje, jakie czynniki – racjonalne i pozaracjonalne – wpływają na zachowania i działania Rosji. Uważa, że miejsce Rosji  – w rozumieniu historycznym i geopolitycznym – jest w Europie i należy wspólnie poszukiwać sposobów przezwyciężenia obecnych napięć, aby razem stawić czoła wyzwaniom współczesnego świata. Rzeczowe, dyplomatyczne analizy przeplatane są anegdotami i cytatami z wielu niezwykle interesujących i szczerych rozmów z (nie zawsze ujawnianymi z nazwiska) wybitnymi intelektualistami, politykami i biznesmenami, co nadaje książce szczególną wartość. Von Fritsch jest wybitnym znawcą Rosji, dostrzega także i szanuje obawy i doświadczenia historyczne państw sąsiadujących z Rosją i Niemcami (Polska, kraje bałtyckie). Przekonuje, że formuła Unii Europejskiej dobrze służy leczeniu zbiorowych traum i łagodzeniu resentymentów oraz budowaniu pokojowej przyszłości kontynentu, a unijna polityka multilateralizmu daje nadzieję na przeciwstawienie się tendencjom multipolarnym, które forsuje Rosja. Książka pozwala poznać i zrozumieć skomplikowaną grę interesów naszych najbliższych sąsiadów i uświadamia, jak pilnie Polska potrzebuje mądrej polityki zagranicznej.

37035
Eлектронна книга

Quodlibet [co się podoba]. Studia dominikańskie

Praca zbiorowa

W książce zebrano 10 rozpraw dominikanów związanych z krakowskim Kolegium Filozoficzno-Teologicznym. Wszyscy mieli całkowitą wolność w wyborze tematu i podejściu do niego. Postawiono jeden warunek: inspiracją dla tekstu ma być słowo. Efektem jest symfonia, w której wielość wątków i wariacji jednoczy wspólny temat. I tak na przykład historycy, odtwarzając początki dominikanów w Polsce, odpowiadają na pytanie o krytykę tekstów źródłowych. Liturgista mierzy się z przekładem tekstów liturgicznych na języki narodowe. Psychiatra i moralista bada granicę między objawieniem a patologicznym słyszeniem głosów. Filozof formułuje normy etyki wypowiedzi i ostrzega przed inflacją sensu.

37036
Eлектронна книга

R Bioinformatics Cookbook. Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis

Dan MacLean

Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples.This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.

37037
Eлектронна книга

R Bioinformatics Cookbook. Utilize R packages for bioinformatics, genomics, data science, and machine learning - Second Edition

Dan MacLean

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools.This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses.By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.

37038
Eлектронна книга

R: Data Analysis and Visualization. Click here to enter text

Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, ...

The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.

37039
Eлектронна книга

R Data Analysis Cookbook. Customizable R Recipes for data mining, data visualization and time series analysis - Second Edition

Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data.This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way.By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.

37040
Eлектронна книга

R Data Analysis Projects. Build end to end analytics systems to get deeper insights from your data

Gopi Subramanian

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle.You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.

37041
Eлектронна книга

R Data Mining. Implement data mining techniques through practical use cases and real-world datasets

Andrea Cirillo

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.

37042
Eлектронна книга

R Data Science Essentials. R Data Science Essentials

Raja B. Koushik, Sharan Kumar Ravindran

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.

37043
Eлектронна книга

R Data Structures and Algorithms. Increase speed and performance of your applications with effi cient data structures and algorithms

PKS Prakash, Achyutuni Sri Krishna Rao

In this book, we cover not only classical data structures, but also functional data structures.We begin by answering the fundamental question: why data structures? We then move on to cover the relationshipbetween data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth.Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors.With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort.

37044
Eлектронна книга
37045
Eлектронна книга

R Data Visualization Recipes. A cookbook with 65+ data visualization recipes for smarter decision-making

Vitor Bianchi Lanzetta

R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using ShinyBy the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.

37046
Eлектронна книга

R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet

PKS Prakash, Achyutuni Sri Krishna Rao

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.

37047
Eлектронна книга

R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition

Mark Hodnett, Joshua F. Wiley

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.

37048
Eлектронна книга

R Deep Learning Projects. Master the techniques to design and develop neural network models in R

Yuxi (Hayden) Liu, Pablo Maldonado

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.

37049
Eлектронна книга

R for Data Science Cookbook. Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

Yu-Wei, Chiu (David Chiu)

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.

37050
Eлектронна книга
37051
Eлектронна книга
37052
Eлектронна книга
37053
Eлектронна книга
37054
Eлектронна книга

R Machine Learning By Example. Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

Dipanjan Sarkar, Raghav Bali

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.

37055
Eлектронна книга
37056
Eлектронна книга

R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Dr. Sunil Kumar Chinnamgari

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.