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.

Quality assurance is an essential aspect for ensuring the success of corporations worldwide. Consistent quality requirements across organizations of similar types ensure that these requirements can be accurately and easily evaluated. Shaping the Future Through Standardization is an essential scholarly book that examines quality and standardization within diverse organizations globally with a special focus on future perspectives, including how standards and standardization may shape the future. Featuring a wide range of topics such as economics, pedagogy, and management, this book is ideal for academicians, researchers, decision makers, policymakers, managers, corporate professionals, and students.

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.

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.

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.

"Beautiful Data" is both a history of big data and interactivity, and a sophisticated meditation on ideas about vision and cognition in the second half of the twentieth century. Contending that our forms of attention, observation, and truth are contingent and contested, Orit Halpern historicizes the ways that we are trained, and train ourselves, to observe and analyze the world. Tracing the postwar impact of cybernetics and the communication sciences on the social and human sciences, design, arts, and urban planning, she finds a radical shift in attitudes toward recording and displaying information. These changed attitudes produced what she calls communicative objectivity: new forms of observation, rationality, and economy based on the management and analysis of data. Halpern complicates assumptions about the value of data and visualization, arguing that changes in how we manage and train perception, and define reason and intelligence, are also transformations in governmentality. She also challenges the paradoxical belief that we are experiencing a crisis of attention caused by digital media, a crisis that can be resolved only through intensified media consumption.

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.

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