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Summary: Leverage the power of Java and its associated machine learning libraries to build powerful predictive models Key Features Solve predictive modeling problems using the most popular machine learning Java libraries Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries Practical examples, tips, and tricks to help you understand applied machine learning in Java Book Description As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. What you will learn Discover key Java machine learning libraries Implement concepts such as classification, regression, and clustering Develop a customer retention strategy by predicting likely churn candidates Build a scalable recommendation engine with Apache Mahout Apply machine learning to fraud, anomaly, and outlier detection Experiment with deep learning concepts and algorithms Write your own activity recognition model for eHealth applications Who this book is for If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you.

Summary: "The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more"-- Provided by publisher.

Summary: Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key Features Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML Leverage SQL syntax to train, evaluate, test, and use ML models Discover how BigQuery works and understand the capabilities of BigQuery ML using examples Book Description BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML. What you will learn Discover how to prepare datasets to build an effective ML model Forecast business KPIs by leveraging various ML models and BigQuery ML Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks Find out how to invoke a trained TensorFlow model directly from BigQuery Get to grips with BigQuery ML best practices to maximize your ML performance Who this book is for This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. Yo...

Summary: A hands-on, readable guide to machine learning with R. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights and make new predictions. The 3rd edition features newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning.

Summary: Making Media Theory is about the study, practice, and hands-on design of media theory. It looks at experimental research methods and engages in media analysis, inviting readers to respond to and shape the materiality of media while carefully considering the implications of living in a technoculture. The author walks readers through the creation of digital objects to think with, where critical design practices serve as tools for exploring social and philosophical issues related to technological being and becoming.

Summary: This book outlines the common architectures used for deriving meaningful data from sensors. In today's world we are surrounded by sensors collecting various types of data about us and our environments. These sensors are the primary input devices for wearable computers, internet-of-things, and other mobile devices. This book provides the reader with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field. The information is presented in way that allows readers to associate the examples with their daily lives for better understanding of the concepts. Making Sense of Sensors starts with an overview of the general pipeline to extract meaningful data from sensors. It then dives deeper into some commonly used sensors and algorithms designed for knowledge extraction. Practical examples and pointers to more information are used to outline the key aspects of Multimodal recognition. The book concludes with a discussion on relationship extraction, knowledge representation, and management.

Summary: At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the products intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career.

Summary: AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis.

Summary: This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection. Provides a thorough look into the variety of mathematical theories of machine learning Presented in four parts, allowing for readers to easily navigate the complex theories Includes extensive empirical studies on both the synthetic and real application time series data.

Summary: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.

Summary: "The fourth edition of Media and Entertainment Law has been fully updated, analysing some of the most recent judgments in media law from across the United Kingdom, such as: Cliff Richard v the BBC, Max Schrems v Facebook and the Irish Information Commissioner, developments on the 'right to be forgotten' (NT1 and NT2) and ABC v Daily Telegraph (Sir Philip Green). The book's two main themes are freedom of expression and an individual's right to privacy. Regulation of the communication industries is covered extensively, including discussion of the print press and its online editions following Leveson, traditional broadcasting regulations for terrestrial TV and radio as well as media activities on converged devices, such as tablets, iPads, mobile phone devices and 'on demand' services. Intellectual property law (specifically copyright) in the music and entertainment industries is also explored in the book's later chapters. Also new to this edition are sections on: A focus on freedom of expression: its philosophical foundations; the struggles of those who have fought for it; and the varied ways in which the courts interpret freedom of expression regarding the taking and publishing of photographs. The 'right to be forgotten', data breaches, and the General Data Protection Regulation (GDPR). The media's increasing access to the courts, particularly when considering the privacy of those who are suspected of sexual offences. Press regulators, broadcasting and advertising regulations, and film and video regulations. Election and party-political broadcast regulations, with a focus on social media and recent election fraud. The emergence of online music distribution services, internet radio and free digital streaming music services, and their effect on the music industry. The 4th edition also features a variety of pedagogical features to encourage critical analysis of case law and one's own beliefs"-- Provided by publisher.

Summary: "Examines U.S. metro dailies, the newspapers hit hardest by declines in advertising and circulation, and how they continue to survive and contribute important news coverage despite predictions that they would eventually wither away"-- Provided by publisher.

Summary: "A guide to mining and analyzing data from social media websites. Combines both practical exercises and conceptual lessons on topics like writing a script to tap into an API and even how to analyze the data of an automated Twitter bot."-- provided by publisher.

Summary: "This publication collects essays and projects that attempt to understand photography and artificial intelligence in the present moment through an artistic and historical lens"-- Provided by publisher.

Summary: You'll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You'll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you'll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You'll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud.

Summary: "In an age when young people may confuse online chatter with legitimate news, Navigating the News is the first textbook designed to show students how to recognize credible reporting and how real journalists perform their jobs. The book begins with the basics of how to critically assess news stories, then covers what to look for in everything from community news and crime reporting to business, political and investigative coverage. More than 50 professional journalists share insights on how they gather, edit and report news, and discuss what critical audiences should expect from their news coverage. Students learn how to analyze complex topics including science, environmental and education news, and a series of chapters covers how to approach news from different parts of the world. Navigating the News is aimed at general audiences, not just journalism or communication majors. Given the importance and timeliness of the subject, this book could easily be the core text for general education classes on news and media literacy. The trend toward teaching young people how to understand and assess news is gaining momentum at universities everywhere"-- Provided by publisher.

Summary: "First released in 1987, Near Dark is a vampire film set in the contemporary American Midwest that tells the story of Caleb, a half-vampire trying to decide whether to embrace his vampire nature or return to his human family. The film, an early work of the now-established director Kathryn Bigelow, skilfully mixes genre conventions, combining gothic tropes with those of the Western, road movie and film noir, while also introducing elements of the outlaw romance genre. Stacey Abbott's study of the film addresses it as a genre hybrid that also challenges conventions of the vampire film. The family of vampires who lure Caleb into their nocturnal existence is a central element of the film's innovative power: defined by a nomadic lifestyle, anarchic behaviour, a passion for violence, ambition for eternity, intense family bonds, and a gritty visual appearance. The vampires are morally ambiguous and undermine the class structures that have historically defined stories of the undead. These are not aristocrats but instead they capture the allure and horror of the disenfranchised and the underclass. The film is sumptuous in its aesthetic design, offering a nuanced and haunting presentation of its monstrous protagonists who stalk the Midwestern backroads and desert landscape. While it remains Bigelow's only foray into horror to date, its innovation showcases the creativity and artistic richness of the genre without sacrificing its visceral qualities. As Abbott describes, Near Dark was crucial in consolidating Bigelow's standing as a director of significance at an early point in her career, not simply because of her visual art background, but because of the way in which she would from Near Dark onward re-envision other traditionally mainstream genres of filmmaking"-- Provided by publisher.

Summary: "This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide a broad coverage to the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring driving safety as well as preventing accidents from all possible causes. Self-contained and unified in presentation, the book explains in details the fundamental solutions of vehicle's perception, vehicle's decision-making, and vehicle's action-taking in a pedagogic order. Besides the fundamental knowledge and concepts of intelligent vehicle's perception, decision and action, this book includes a comprehensive set of real-life application scenarios in which intelligent vehicles will play a major role or contribution. These case studies of real-life applications will help motivate students to learn this exciting subject. With concise and simple explanations, and boasting a rich set of graphical illustrations, the book is an invaluable source for both undergraduate and postgraduate courses, on artificial intelligence, intelligent vehicle, and robotics, which are offered in automotive engineering, computer engineering, electronic engineering, and mechanical engineering. In addition, the book will help strengthen the knowledge and skills of young researchers who want to venture into the research and development of artificial self-intelligence for intelligent vehicles of the future"-- Provided by publisher.

Summary: "This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide a broad coverage to the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring driving safety as well as preventing accidents from all possible causes. Self-contained and unified in presentation, the book explains in details the fundamental solutions of vehicle's perception, vehicle's decision-making, and vehicle's action-taking in a pedagogic order. Besides the fundamental knowledge and concepts of intelligent vehicle's perception, decision and action, this book includes a comprehensive set of real-life application scenarios in which intelligent vehicles will play a major role or contribution. These case studies of real-life applications will help motivate students to learn this exciting subject. With concise and simple explanations, and boasting a rich set of graphical illustrations, the book is an invaluable source for both undergraduate and postgraduate courses, on artificial intelligence, intelligent vehicle, and robotics, which are offered in automotive engineering, computer engineering, electronic engineering, and mechanical engineering. In addition, the book will help strengthen the knowledge and skills of young researchers who want to venture into the research and development of artificial self-intelligence for intelligent vehicles of the future"-- Provided by publisher.

Summary: "In this current period of uncertainty and introspection in the media, New Journalisms focuses not just on new challenges facing journalism, but also seeks to capture a wide range of new practices that are being employed across a diversity of media. This edited collection explores how these new practices can lead to a re-imagining of journalism in terms of practice, theory and pedagogy, bringing together high-profile academics, emerging researchers and well-known journalism practitioners. The book's opening chapters assess the challenges of loss of trust and connectivity, shifting professional identity and the demise of local journalism. A section on new practices evaluates algorithms, online participatory news websites and verification. Finally, the collection explores whether new pedagogies offer potential routes to new journalisms. Representing a timely intervention in the debate and providing sustainable impact through its forward-looking focus, New Journalisms is essential reading for students of journalism and media studies"-- Provided by publisher.

Summary: "Netflix cofounder Reed Hastings reveals for the first time the unorthodox culture behind one of the world's most innovative, imaginative, and successful companies. There's never before been a company like Netflix. Not only because it has led a revolution in the entertainment industries; or because it generates billions of dollars in annual revenue; or even because it is watched by hundreds of millions of people in nearly 200 countries. When Reed Hastings co-founded Netflix, he developed a set of counterintuitive and radical management principles, defying all tradition and expectation, which would allow the company to reinvent itself over and over on the way to becoming one of the most loved brands in the world. Rejecting the conventional wisdom under which other companies operate, Reed set new standards, valuing people over process, emphasizing innovation over efficiency, and giving employees context, not controls. At Netflix, adequate performance gets a generous severance and hard work is irrelevant. At Netflix, you don't try to please your boss, you practice radical candor instead. At Netflix, employees never need approval, and the company always pays top of market. When Hastings and his team first devised these principles, the implications were unknown and untested, but over just a short period of time they have led to unprecedented flexibility, speed, and boldness. The culture of freedom and responsibility has allowed the company to constantly grow and change as the world, and its members' needs, have also transformed. Here for the first time, Hastings and Erin Meyer, bestselling author of The Culture Map and one of the world's most influential business thinkers, dive deep into the controversial philosophies at the heart of the Netflix psyche, which have generated results that are the envy of the business world. Drawing on hundreds of interviews with current and past Netflix employees from around the globe and never-before-told stories of trial and error from his own career, No Rules Rules is the full, fascinating, and untold story of a unique company making its mark on the world"-- Provided by publisher.

Summary: "The recipient of multiple Peabody and Murrow awards, Clarissa Ward is a world-renowned conflict reporter. In this strange age of crisis where there really is no front line, she has moved from one hot zone to the next. With multiple assignments in Syria, Egypt, and Afghanistan, Ward, who speaks seven languages, has been based in Baghdad, Beirut, Beijing, and Moscow. She has seen and documented the violent remaking of the world at close range. With her deep empathy, Ward finds a way to tell the hardest stories. On All Fronts is the riveting account of Ward's singular career and of journalism in this age of extremism. Following a privileged but lonely childhood, Ward found her calling as an international war correspondent in the aftermath of 9/11. From her early days in the field, she was embedding with marines at the height of the Iraq War and was soon on assignment all over the globe. But nowhere does Ward make her mark more than in war-torn Syria, which she has covered extensively with courage and compassion. From her multiple stints entrenched with Syrian rebels to her deep investigations into the Western extremists who are drawn to ISIS, Ward has covered Bashar al-Assad's reign of terror without fear. In 2018, Ward rose to new heights at CNN and had a son. Suddenly, she was doing this hardest of jobs with a whole new perspective"-- Provided by publisher.

Summary: This book reviews the literature on hand posture estimation using generative methods, identifying the current gaps, such as sensitivity to hand shapes, sensitivity to a good initial posture, difficult hand posture recovery in cases of loss in tracking, and lack of addressing multiple objectives to maximize accuracy and minimize computational cost. To fill these gaps, it proposes a new 3D hand model that combines the best features of the current 3D hand models in the literature. It also discusses the development of a hand shape optimization technique. To find the global optimum for the single-objective problem formulated, it improves and applies particle swarm optimization (PSO), one of the most highly regarded optimization algorithms and one that is used successfully in both science and industry. After formulating the problem, multi-objective particle swarm optimization (MOPSO) is employed to estimate the Pareto optimal front as the solution for this bi-objective problem. The book also demonstrates the effectiveness of the improved PSO in hand posture recovery in cases of tracking loss. Lastly, the book examines the formulation of hand posture estimation as a bi-objective problem for the first time. The case studies included feature 50 hand postures extracted from five standard datasets, and were used to benchmark the proposed 3D hand model, hand shape optimization, and hand posture recovery.

Summary: This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques. Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.

Summary: "Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use."--Page 4 of cover.

Summary: Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- Source other than the Library of Congress.

Summary: Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- Source other than the Library of Congress.

Summary: This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on ‘scikit-learn,’ but other Python libraries like ‘Gensim’ or ‘PyTorch’ will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models.

Summary: In this revised and expanded edition of Predictably Irrational, Dan Ariely explores the hidden forces that shape our decisions, including some of the causes responsible for the current economic crisis. Bringing a much-needed dose of sophisticated psychological study to the realm of public policy, Ariely offers his own insights into the irrationalities of everyday life, the decisions that led us to the financial meltdown of 2008, and the general ways we get ourselves into trouble.

Summary: "Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis Integration with cutting-edge Big Data approaches Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors"-- Provided by publisher.

Summary: Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.

Summary: "The book introduces probabilistic machine learning concepts to civil engineering students and professionals, who typically do not have the background necessary to understand the subject from a purely computer science perspective. It presents key approaches among the three sub-fields of machine learning: supervised, unsupervised, and reinforcement learning. The methods are demonstrated through step-by-step examples and copius illustrations in order to simplify abstract concepts. The book will prepare readers to access the vast body of literature from the field of machine learning"-- Provided by publisher.

Summary: "Producing New and Digital Media is your essential guide to understanding new media, taking a deep dive into such topics as the cultural and social impacts of the web, the importance of digital literacy, and creating in an online environment. This cutting-edge text provides an introductory, hands-on approach to creating user-generated content, coding, cultivating an online brand, and storytelling in new and digital media. In showing you how to navigate the world of digital media and complete digital tasks, this book not only teaches you how to use the web, but also helps you understand why you use it. Key features for the 2nd edition include: Coverage of up-to-date forms of communication on the web: memes, viral videos, social media, and more pervasive types of online languages. New chapters on YouTube influencers and on-demand subscription television. Each chapter has media literacy sidebars, sample assignments, and activities. Updates to the companion website additional materials for students and instructors Thoughtful, entertaining and enlightening, this is the fundamental textbook for students of new and digital media, digital culture and media literacy, as well as a useful resource for anyone wanting to understand and develop their presence in our digital world"-- Provided by publisher.

Summary: Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.

Summary: Combining classical scenario thinking (the gentle art of perception) with the analytical power of big data and artificial intelligence, Real Time Strategypresents the decision making of the future which enables decision makers to develop dynamic strategies, monitor their validity, and react faster.

Summary: How do journalists know what they know? Who gets to decide what good journalism is and when it's done right? This text contends that, despite thoughtful explorations of the role of publics in journalism, the profession's methodologies and practices still don't adequately address matters of race, gender, intersectionality and settler colonialism. Drawing on their five years of research with journalists in the US and Canada, in a variety of news organizations from startups and freelancers to mainstream media, the authors investigate modern journalism's founding ideals and methods and their relationship to power to examine emerging multiple journalisms.

Summary: This book examines journalism’s ability to promote and foster cohesive and collective action while critically examining its place in the intensifying battle to maintain a society’s social order. From chapters discussing the challenges journalists face in covering populism and Donald Trump, to chapters about issues of race in the news, intersections of journalism and nationalism, and increased mobilities of audiences and communicators in a digital age, Reimagining Journalism and Social Order in a Fragmented Media World focuses on the pitfalls and promises of journalism in moments of social contestation. Rich with perspectives from across the globe, this book connects journalism studies to critical scholarship on social order and social control, nationalism, social media, geography, and the function of news as a social sphere. In a fragmented media world and in times of social contestation, Reimagining Journalism and Social Order in a Fragmented Media World provides readers with insights as to how journalism operates in order to highlight―and enhance―elements and actions that bring about order. This book was originally published as a special issue of Journalism Studies and a special issue of Journalism Practice.

Summary: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

Summary: This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it's forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.

Summary: "Americans say that reading, watching, or listening to the news is a leading cause of stress. Of course journalists, as watchdogs and public informants, must disseminate information that is inherently negative, but experts argue that the news media's emphasis on the problem has had a negative effect on the public, the press itself, and democracy. At the same time, the past sixty years have seen a rise of journalistic practices that purport to cover the news beyond the typical problem-based narrative. These genres of journalistic reporting are not positive news or fluff reporting: They are rigorous reporting philosophies and practices that share a common goal - reporting beyond the problem-based narrative, thereby exemplifying a commitment to the social responsibility theory of the press, which asserts that journalists have a duty to consider society's best interests. However, there is little academic or professional understanding of these journalistic approaches. As such, this book provides an in-depth examination of socially-responsible news reporting practices, such as constructive journalism, solutions journalism, and peace journalism. Each chapter focuses on one reporting form, defining it and detailing its evolution and status among scholars and practitioners, as well as discussing its known effects and future direction. This edited volume is the first academic book published on these forms of reporting in the United States. It provides a comprehensive resource that explores the theoretical underpinnings of these journalistic genres that grounds these approaches and allows for a coherent line of research to follow as these approaches evolve"-- Provided by publisher.

Summary: Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today's petroleum and reservoir engineer to optimize more complex developments.

Summary: Discovery is central to academic activities at all levels and is a major focus for libraries and museums. Of all the parts of modern library provision, discovery services are the most clearly affected by developments in IT, from databases to search engines to linked data to machine learning. It is crucial to the relationship between libraries and their communities. This book will help its readers learn how to adapt in a fast changing area to continue to provide a high level of service. Resource Discovery for the Twenty-First Century Library contains a range of contributions analysing the ways in which libraries are tackling the challenges facing them in discovery in the (post)-Google era. Chapters are written by experts, both global and local - describing specific areas of discovery and local implementations and ideas. The book will help with enhancing discovery both inbound - making locally held resources globally discoverable, and outbound - making global resources locally discoverable, in ways which are relevant to your user community. Content covered includes: a survey of what resource discovery is today ; case studies from around the world of interesting approaches to discovery ; analysis of how users approach discovery ; how to understand and make the best use of Internet search engines ; using limited resources to help users and collections ; linked open data and discovery; and the future of discovery. This book will be useful for subject librarians and others who give direct support to library users, digital library technicians, managers, staff with responsibility for managing electronic resources, metadata and discovery specialists, trainers and user education specialists. It will also be of use to curators and others who give direct support to researchers, managers of digitisation and cataloguing products, IT staff, trainers and user education specialists.

Summary: "Russia's Regional Media: Struggle and Survival in the Heartland takes an intimate look at the enormous challenges and small victories experienced by local Russian journalists across the post-perestroika and Putin eras. The book examines 13 years of journalists' struggles for independence and meaning as they weigh their professional goals and community obligations against their growing dependence on local elite. Russia's sub-national levels - its provinces and communities -- remain understudied but important. Local newspapers are the only means by which news reaches many rural Russians, and Russia's "heartland" regions are a significant source of support for the current national regime. The book contributes importantly to our understanding of Russian journalism, and to our understanding of local journalism generally, an increasingly vulnerable institution in countries around the world. Russia's Regional Media seeks answers to a number of questions: How do challenging political-economic environments constrain and guide the ways Russian journalists imagine their roles and do their work? Can journalists represent their regions in meaningful, distinct ways, and are they seeking autonomy or mere survival? How does local Russian journalism fit within the global context of local journalism? Russia's Regional Media will serve as a valuable companion text for senior-level or graduate courses on Russian media and culture, global media, local journalism, media production, and media sociology. The book will also be of value to anyone interested in journalism's ongoing challenges in a diverse, changing world"-- Provided by publisher.

Summary: "An anticipated third edition of the go-to text on agenda-setting"-- Provided by publisher.

Summary: "The five-time Emmy Award-winning investigative reporter and New York Times bestselling author of Stonewalled and The Smear uncovers how partisan bias and gullibility are destroying American journalism"-- Provided by publisher.

Summary: Smart Card Security: Applications, Attacks, and Countermeasures provides an overview of smart card technology and explores different security attacks and countermeasures associated with it. It covers the origin of smart cards, types of smart cards, and how they work. It discusses security attacks associated with hardware, software, data, and users that are a part of smart card-based systems. The book starts with an introduction to the concept of smart cards and continues with a discussion of the different types of smart cards in use today, including various aspects regarding their configuration, underlying operating system, and usage. It then discusses different hardware- and software-level security attacks in smart card-based systems and applications and the appropriate countermeasures for these security attacks. It then investigates the security attacks on confidentiality, integrity, and availability of data in smart card-based systems and applications, including unauthorized remote monitoring, communication protocol exploitation, denial of service (DoS) attacks, and so forth, and presents the possible countermeasures for these attacks. The book continues with a focus on the security attacks against remote user authentication mechanisms in smart card-based applications and proposes a possible countermeasure for these attacks. Then it covers different communication standards for smart card-based applications and discusses the role of smart cards in various application areas as well as various open-source tools for the development and maintenance of smart card-based systems and applications. The final chapter explains the role of blockchain technology for securing smart card-based transactions and quantum cryptography for designing secure smart card-based algorithms. Smart Card Security: Applications, Attacks, and Countermeasures provides you with a broad overview of smart card technology and its various applications