The advancement of technology in today's world has led to the progression of several professional fields. This includes the classroom, as teachers have begun using new technological strategies to increase student involvement and motivation. ICT innovation including virtual reality and blended learning methods has changed the scope of classroom environments across the globe; however, significant research is lacking in this area. ICTs and Innovation for Didactics of Social Sciences is a fundamental reference focused on didactics of social sciences and ICTs including issues related to innovation, resources, and strategies for teachers that can link to the transformation of social sciences teaching and learning as well as societal transformation. While highlighting topics such as blended learning, augmented reality, and virtual classrooms, this book is ideally designed for researchers, administrators, educators, practitioners, and students interested in understanding current relevant ICT resources and innovative strategies for the didactic of social sciences and didactic possibilities in relation to concrete conceptual contents, resolution of problems, planning, decision making, development of social skills, attention, and motivation promoting a necessary technological literacy.

This unique text/reference reviews the key principles and techniques in conceptual modelling which are of relevance to specialists in the field of cultural heritage. Information modelling tasks are a vital aspect of work and study in such disciplines as archaeology, anthropology, history, and architecture. Yet the concepts and methods behind information modelling are rarely covered by the training in cultural heritage-related fields. With the increasing popularity of the digital humanities, and the rapidly growing need to manage large and complex datasets, the importance of information modelling in cultural heritage is greater than ever before. To address this need, this book serves in the place of a course on software engineering, assuming no previous knowledge of the field. Topics and features: Presents a general philosophical introduction to conceptual modelling Introduces the basics of conceptual modelling, using the ConML language as an infrastructure Reviews advanced modelling techniques relating to issues of vagueness, temporality and subjectivity, in addition to such topics as metainformation and feature redefinition Proposes an ontology for cultural heritage supported by the Cultural Heritage Abstract Reference Model (CHARM), to enable the easy construction of conceptual models Describes various usage scenarios and applications of cultural heritage modelling, offering practical tips on how to use different techniques to solve real-world problems This interdisciplinary work is an essential primer for tutors and students (at both undergraduate and graduate level) in any area related to cultural heritage, including archaeology, anthropology, art, history, architecture, or literature. Cultural heritage managers, researchers, and professionals will also find this to be a valuable reference, as will anyone involved in database design, data management, or the conceptualization of cultural heritage in general. Dr. Cesar Gonzalez-Perez is a Staff Scientist at the Institute of Heritage Sciences (Incipit), within the Spanish National Research Council (CSIC), Santiago de Compostela, Spain.

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications Key Features Build independent machine learning (ML) systems leveraging the best features of R 3.5 Understand and apply different machine learning techniques using real-world examples Use methods such as multi-class classification, regression, and clustering Book DescriptionGiven the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You'll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you'll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You'll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you'll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you'll be equipped with the skills to deploy ML techniques in your own projects or at work. What you will learn Prepare data for machine learning methods with ease Understand how to write production-ready code and package it for use Produce simple and effective data visualizations for improved insights Master advanced methods, such as Boosted Trees and deep neural networks Use natural language processing to extract insights in relation to text Implement tree-based classifiers, including Random Forest and Boosted Tree Who this book is forThis book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Learn advanced techniques to improve the performance and quality of your predictive models Key Features Use ensemble methods to improve the performance of predictive analytics models Implement feature selection, dimensionality reduction, and cross-validation techniques Develop neural network models and master the basics of deep learning Book DescriptionPython is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics. By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis. What you will learn Use ensemble algorithms to obtain accurate predictions Apply dimensionality reduction techniques to combine features and build better models Choose the optimal hyperparameters using cross-validation Implement different techniques to solve current challenges in the predictive analytics domain Understand various elements of deep neural network (DNN) models Implement neural networks to solve both classification and regression problems Who this book is forMastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Luciano Floridi presents an innovative approach to philosophy, conceived as conceptual design. He explores how we make, transform, refine, and improve the objects of our knowledge. His starting point is that reality provides the data, to be understood as constraining affordances, and we transform them into information, like semantic engines. Such transformation or repurposing is not equivalent to portraying, or picturing, or photographing, or photocopying anything. It is more like cooking: the dish does not represent the ingredients, it uses them to make something else out of them, yet the reality of the dish and its properties hugely depend on the reality and the properties of the ingredients. Models are not representations understood as pictures, but interpretations understood as data elaborations, of systems. Thus, Luciano Floridi articulates and defends the thesis that knowledge is design and philosophy is the ultimate form of conceptual design. Although entirely independent of Floridi's previous books, The Philosophy of Information (OUP 2011) and The Ethics of Information (OUP 2013), The Logic of Information both complements the existing volumes and presents new work on the foundations of the philosophy of information.

This book is about the definition of the Shannon measure of Information, and some derived quantities such as conditional information and mutual information. Unlike many books, which refer to the Shannon's Measure of information (SMI) as 'Entropy,' this book makes a clear distinction between the SMI and Entropy.In the last chapter, Entropy is derived as a special case of SMI.Ample examples are provided which help the reader in understanding the different concepts discussed in this book. As with previous books by the author, this book aims at a clear and mystery-free presentation of the central concept in Information theory - the Shannon's Measure of Information.This book presents the fundamental concepts of Information theory in a friendly-simple language and is devoid of all kinds of fancy and pompous statements made by authors of popular science books who write on this subject. It is unique in its presentation of Shannon's measure of information, and the clear distinction between this concept and the thermodynamic entropy.Although some mathematical knowledge is required by the reader, the emphasis is on the concepts and their meaning rather on the mathematical details of the theory.

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This book analyzes the methods, technologies, standards, and languages to structure and describe data in their entirety. It reveals common features, hidden assumptions, and ubiquitous patterns among these methods and shows how data are actually structured and described independently from particular trends and technologies.

Examples of data structuring methods analyzed critically include: Encodings (e.g. Unicode) Identifiers and Identifier systems (e.g. ISBN) File systems Database Systems (record databases, relational databases, NoSQL...) Data structuring languages (JSON, XML, CSV, RDF...) markup languages (SGML, HTML, TEI, Markdown...) Schema languages (BNF, XSD, RDFS, OWL, SQL...) Conceptual modeling languages (ERM, ORM, UML, DSL...) Conceptual diagrams

It is shown how particular method of data structuring and description can best be categorized by their primary purpose. The study further exposes five basic paradigms that deeply shape how data is structured and described in practice. The third results is a pattern language of data structuring. Patterns show problems and solutions which occur over and over again in data. Each pattern is described with its benefits, consequences, pitfalls, and relations to other patterns.

The results can help to better understand data and its actual forms, both for consumption and creation of data. Possible applications include data analysis, data modeling, data archaeology, and data literacy.

Daten werden uberall gesammelt. Jeder Kauf, ob online oder offline, jede Autofahrt und jede Benutzung des Smartphones erzeugt Daten, die gespeichert werden. So entstehen Datenberge, die in atemberaubendem Tempo wachsen - fur 2020 geht man von 40 Billionen Gigabytes aus. Aber was passiert dann mit diesen Daten? Wie werden sie ausgewertet? Und wer macht das? Holger Aust nimmt Sie mit auf einen unterhaltsamen Ausflug in die wunderbare Welt der Data Science. Sein Buch richtet sich an alle, die schon immer wissen wollten, wie Maschinen anhand von Daten lernen und ob sie dadurch (kunstliche) Intelligenz erlangen. Sie erfahren naturlich auch, was neuronale Netze und Deep Learning eigentlich mit all dem zu tun haben. In leicht verstandlichem Stil erhalten Sie ausserdem Einblicke in die Funktionsweise der wichtigsten Algorithmen und lernen konkrete Beispiele, Herausforderungen und Risiken aus der Praxis kennen: Sie erfahren etwa, wie Mobilfunkanbieter ihre Kunden bei Laune halten, wie Erdbebenvorhersage funktioniert und warum auch Computer zum Schubladendenken neigen.