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The Press We Deserve

The Press We Deserve

Originally published in 1970. This book takes an extremely critical look at the British Press and explodes complacently held views of the time about the merits and virtues of British newspapers. From its polemical introduction by the editor it continues with chapters by a strong host of contributors to set the press in historical context consider Fleet Street’s methods and look at the effect of advertising. Particular aspects investigated are that of women’s journalism sport financial journalism and reviewing. Final chapters look at the underground fringe press provincial papers and a comparison with other countries’ press. | The Press We Deserve

GBP 31.99
1

Racism and the Press

Racism and the Press

Originally published in 1991. This book presents the results of an interdisciplinary study of the press coverage of ethnic affairs. Examples are drawn mainly from British and Dutch newspapers but data from other countries are also reviewed. Besides providing the reader with a thorough content analysis of the material the book is the first to introduce a detailed discourse analytical approach to the study of the ways in which ethnic minorities are portrayed in the press. The approach focuses on the topics overall news report schemata local meanings style and rhetoric of news reports. Highly original accomplished and penetrating the book is the fruit of a decade of research into the question of racism and the press important for ethnic studies mass communication and media studies sociology and linguistics.

GBP 38.99
1

Machine Learning for Managers

Machine Learning for Managers

Machine learning can help managers make better predictions automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about but who lack a technical background in computer science statistics or math. The book describes in plain language what machine learning is and how it works. In addition it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations. | Machine Learning for Managers

GBP 29.99
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A Press Divided Newspaper Coverage of the Civil War

A Press Divided Newspaper Coverage of the Civil War

Now in paperback A Press Divided provides new insights regarding the sharp political divisions that existed among the newspapers of the Civil War era. These newspapers were divided between North and South - and also divided within the North and South. These divisions reflected and exacerbated the conflicts in political thought that caused the Civil War and the political and ideological battles within the Union and the Confederacy about how to pursue the war. In the North dissenting voices alarmed the Lincoln administration to such a degree that draconian measures were taken to suppress dissenting newspapers and editors while in the South the Confederate government held to its fundamental belief in freedom of speech and was more tolerant of political attacks in the press. This volume consists of eighteen chapters on subjects including newspaper coverage of the rise of Lincoln press reports on George Armstrong Custer Confederate women war correspondents Civil War photojournalists newspaper coverage of the Emancipation Proclamation and the suppression of the dissident press. This book tells the story of a divided press before and during the Civil War discussing the roles played by newspapers in splitting the nation newspaper coverage of the war and the responses by the Union and Confederate administrations to press criticism. | A Press Divided Newspaper Coverage of the Civil War

GBP 31.99
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A Narrative History of the American Press

A Narrative History of the American Press

Beginning with the American Revolution and spanning over two hundred years of American journalism A Narrative History of the American Press provides an overview of the events institutions and people who have shaped the press from the creation of the First Amendment to today. Gregory A. Borchard’s introductory text helps readers develop an understanding of the role of the press in both the U. S. and world history and how American culture has shaped—and been shaped by—the role of journalism in everyday life. The text along with a rich array of supplemental materials available online provides students with the tools used by both reporters and historians to understand the present through the past allowing readers to use the history of journalism as a lens for implementing their own storytelling reporting and critical analysis skills.

GBP 42.99
1

Machine Learning Concepts Techniques and Applications

Machine Learning Concepts Techniques and Applications

Machine Learning: Concepts Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases self-assessments exercises activities numerical problems and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare Education Business Engineering Applications Ethics of machine learning including Bias Fairness Trust Responsibility Basics of Deep learning important deep learning models and applications Plenty of objective questions Use Cases Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students researchers and professionals so that they can formulate the problems prepare data decide features select appropriate machine learning algorithms and do appropriate performance evaluation. | Machine Learning Concepts Techniques and Applications

GBP 140.00
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Proceedings of the Second International Conference on Press-in Engineering 2021 Kochi Japan

Proceedings of the Second International Conference on Press-in Engineering 2021 Kochi Japan

The Second International Conference on Press-in Engineering (ICPE) 2021 was organized by the International Press-in Association (IPA). The conference is held every three years and the main theme this time is Evolution and Social Contribution of Press-in Engineering for Infrastructure Development and Disaster Prevention and Mitigation. These proceedings contain 2 keynote lectures 3 state-of-the-art lectures and about 60 papers from more than 10 countries. This publication provides good practice guidance on the application of the press-in piling method to satisfy the requirements of geo-structures which are embedded utilizing prefabricated piles. It covers actual examples of the press-in piling method applied to various geo-structures such as temporary and permanent retaining walls cofferdams cut-off walls foundation piles etc. The content addresses the technical and construction issues relating to the selection of the appropriate type of press-in piling method in accordance with required structural design criteria and soil and working conditions. The aim of this publication is to concisely describe practical uses of the press-in piling method for project owners designers contractors academic researchers and other people in the construction industry. | Proceedings of the Second International Conference on Press-in Engineering 2021 Kochi Japan

GBP 68.99
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Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
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The Colonial Periodical Press in the Indian and Pacific Ocean Regions

The Colonial Periodical Press in the Indian and Pacific Ocean Regions

This book clarifies the crucial role of periodical press in the advance of colonial print cultures and public debates in the Indian and Pacific Oceans. The Colonial Periodical Press in the Indian and Pacific Ocean Regions is a venture of the International Group for Studies of Colonial Periodical Press of the Portuguese Empire (IGSCP-PE) which also invests in comparative studies and conceptual discussions. Moving around urban shores of the Indian and Pacific Oceans it approaches the crucial role of periodical press in the development of colonial print cultures and public debates in these regions. By being mostly focused on press from spaces and peoples under the domain of the Portuguese Empire it addresses a bibliographical gap in international discussions moved by the field. The outcome reflects an investment in offering decentred and de-nationalized approaches to the colonial print cultures and press histories under study working as a platform for regional dialogues and comparative perspectives. The studies presented allow a better understanding of transits and connections of both an imperial and a trans-imperial nature contributing to the consolidation of comparative approaches in the studies of European empires and colonialisms. This volume is indispensable for scholars and students in media studies modern history cultural studies literary studies and political science.

GBP 130.00
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Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation context awareness analyzing various data sources machine learning algorithms and intelligent services and applications. Further it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning security IoT and data mining the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning machine learning Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health transportation and Internet applications | Applied Machine Learning for Smart Data Analysis

GBP 125.00
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Machine Learning Theory and Practice

Machine Learning Theory and Practice

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization tree-based methods including Random Forests and Boosted Trees Artificial Neural Networks including Convolutional Neural Networks (CNNs) reinforcement learning and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid illustrated with figures and examples. For each machine learning method discussed the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding enabling further exploration Presents worked out suitable programming examples thus ensuring conceptual theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth within limits of what can be taught in a short period of time. Thus the book can provide foundations that will empower a student to read advanced books and research papers. | Machine Learning Theory and Practice

GBP 110.00
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Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation the authors explore how machine learning techniques apply to bioinformatics problems such as electron density map interpretation biclustering DNA sequence analysis and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

GBP 59.99
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Cost-Sensitive Machine Learning

Cost-Sensitive Machine Learning

In machine learning applications practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting validation and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

GBP 59.99
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Stochastic Optimization for Large-scale Machine Learning

Stochastic Optimization for Large-scale Machine Learning

Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms i. e. optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

GBP 150.00
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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology cellular neuroscience neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN) clustering and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
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Machine Learning in Translation

Machine Learning in Translation

Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension this book helps linguists translators and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways in terms of word embeddings chunking of larger meaning units and prediction in translation based upon the broader context. Part Two introduces key tasks including machine translation translation quality assessment and quality estimation and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create manage and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation stressing the need to empower translators with ML knowledge through communication with ML users developers and programmers and with opportunities for continuous learning. This accessible guide is designed for current and future users of ML technologies in localization workflows including students on courses in translation and localization language technology and related areas. It supports the professional development of translation practitioners so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.

GBP 34.99
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Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy suitability and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms architecture design and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model performance evaluation and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning along with recent research developments in healthcare sectors. | Machine Learning for Healthcare Handling and Managing Data

GBP 115.00
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R which includes using various R packages such as glmnet h2o ranger xgboost keras and others to effectively model and gain insight from their data. The book favors a hands-on approach providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book the reader will be exposed to the entire machine learning process including feature engineering resampling hyperparameter tuning model evaluation and interpretation. The reader will be exposed to powerful algorithms such as regularized regression random forests gradient boosting machines deep learning generalized low rank models and more! By favoring a hands-on approach and using real word data the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages understand when and how to tune the various hyperparameters and be able to interpret model results. By the end of this book the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
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Machine Learning for Decision Sciences with Case Studies in Python

Machine Learning for Decision Sciences with Case Studies in Python

This book provides a detailed description of machine learning algorithms in data analytics data science life cycle Python for machine learning linear regression logistic regression and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical oil and gas e-commerce and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised supervised and reinforcement learning. This book is aimed at researchers professionals and graduate students in data science machine learning computer science and electrical and computer engineering. | Machine Learning for Decision Sciences with Case Studies in Python

GBP 160.00
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Green Machine Learning Protocols for Future Communication Networks

Green Machine Learning Protocols for Future Communication Networks

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However heavy resources are required to process and analyze the data which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing memory communication and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications efforts are required toward designing new machine learning protocols and modifying the existing ones which consume less energy i. e. green machine learning protocols. In other words novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication federated learning-based models and protocols for Beyond Fifth Generation networks approaches for cloud-based communications and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications. | Green Machine Learning Protocols for Future Communication Networks

GBP 110.00
1

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas such as game development decision making and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning which is followed by Gaussian process introduction classification regression covariance and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates researchers and professionals in the field of data science and machine learning.

GBP 105.00
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