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Summary: "The second edition of Digital and Social Media Marketing is an up to date, industry-led results-driven guide to digital marketing. Mixing academic theory with practical examples from a range of different organisations worldwide, it provides insight into, and techniques to enable, the creation, development and maintenance of a successful digital presence. This highly regarded textbook has been fully revised to bring the content up to date with the newest digital technologies. With topics including developing an effective digital presence, search engine optimization, and measuring brand awareness, the new edition also looks at digital ethics, General Data Protection Regulation (GDPR) and privacy, Artificial Intelligence (AI) and Machine Learning, and Voice strategies. New international case studies are explored, including Alibaba and Amazon, as well as revised practical exercises in each chapter, enable students to see how the concepts underpinning digital and social media marketing support business success. The book's customisable Digital Business Maturity Model, and the Buyer Persona Spring, offer organisations a clear roadmap for understanding their own levels of technology adoption and digital strategy development. This accessible textbook provides a hands-on, user-friendly platform to turn skills and knowledge into strategic advantage. It is ideal for advanced undergraduate and postgraduate students of digital marketing and marketing strategy, and for practitioners aiming to be at the cutting edge of Digital and Social Media Marketing. Alongside electronic resources for each chapter, this new edition also includes digital learning materials, case studies and exercises available in a supporting online learning environment. The online materials further enhance learners' experience and supports a worldwide learning community"-- Provided by publisher.

Summary: "Built using the hands-on and pioneering Missouri Method, this textbook prepares readers to write about and communicate with people of different backgrounds, offering real-world examples of how to practice excellent journalism and strategic communication that takes culture into account. No matter the communication purpose, this book will help readers engage with difference and the concept of fault lines, and to identify and mitigate bias. It provides guidance on communicating the complexity inherent in issues like crime, immigration, and sports, and working with census data to craft stories or strategic campaigns. Above all, the book encourages readers to reconsider assumptions about race, class, gender, identity, sexual orientation, immigration status, religion, disability, and age, and recognize communicators' responsibilities in shaping national discussions. This new edition addresses the ever-changing political and social climate, differentiates excellent journalism from punditry, and shows the business value of understanding diverse perspectives. A fantastic introduction to this complex but important field, this book is perfect for students, teachers, and early career communicators. The combination of a hands-on approach and pull-out boxes with the diverse voices curated by editors María Len-Rios and Earnest Perry make this an ideal text for the classroom and beyond"-- Provided by publisher.

Summary: Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.

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: "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: This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.

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: Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn: Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel This book is for anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended. Hong Zhou, PhD is a professor of computer science and mathematics and has been teaching c ourses in computer science, data science, mathematics, and informatics at the University of Saint Joseph for more than 15 years. His research interests include bioinformatics, data mining, software agents, and blockchain. Prior to his current position, he was as a Java developer in Silicon Valley. Dr. Zhou believes that learners can develop a better foundation of data mining models when they visually experience them step-by-step, which is what Excel offers. He has employed Excel in teaching data mining and finds it an effective approach for both data mining learners and educators.

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: Saransh works at a life insurance company, as part of the Special Projects Group (SPG). Their current project is top-secret: the development of an Artificial Intelligence system that will leave 552 branch-level employees redundant overnight. Because of site-specific customizations, however, the system needs to collect information from the company's various branches. Thus, begins a cycle in which Saransh travels across the country, interviewing the very people that his machine will replace soon. Meanwhile, his conscientious ex-journalist girlfriend Jyoti repeatedly questions Saransh's complicity in the impending destruction of hundreds of lives. The Machine is Learning is a novel about twenty-first-century workplaces, love and the impact of technology in all of our lives. It interrogates a world order that accommodates guilt but offers no truly ethical course correction.

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: "This book is a collection of research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field"-- Provided by publisher.

Summary: This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions

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: "Investigative Journalism, Democracy and the Digital Age explores watchdog reporting in the digital age. Mapping new forms of global collaborative investigative journalism, the book debunks the myth that traditional newsrooms and investigative journalism are dying, and shows how journalists are adapting and experimenting. The book brings together expert analysis and lively examples of investigative journalism in developed economies, with a close focus on the United States, United Kingdom and Australia. Combining decades of investigative journalism analyses with expert interviews with watchdog reporters from across the globe, the author shows us how investigative journalism thrives despite significant challenges, and explores why investigative journalism is integral to democratic accountability and public trust in the news in a fake news era"-- Provided by publisher.

Summary: Deep learning tools could very well end up with a substantially higher standard of accuracy and reliability in the recognition of physical objects, in some instances more advanced than individual overall human performance. Deep learning is an outlet of understanding, or perhaps studying, that takes advantage of a number of levels of non-linear processor jobs to discover how you can make representations of highly effective daily processes unswervingly from computer data.The first chapter refers to deep learning. Chapter 2 shows that when provided with genomic variance computer data from a variety of people, calculating the chance of complicated populace hereditary designs can often be improbable. Chapter 3 looks at how live-cell imaging provides you with started out a thrilling range into the function cellular heterogeneity performs in vibrant, subsistence devices.Chapter 4 looks at how protein contacts provide you with crucial information and facts for the comprehension of protein frameworks. Chapter 5 suggests a structure for foretelling updates in electronic community end user behavior. Chapter 6 looks at how precise computational recognition of promoters continues to be an issue.Chapter 7 shows that an innovative intrusion detection system ( IDS ) making use of a deep neural network ( DNN ) is offered to improve the safety of in-vehicular system. Chapter 8 looks at how event identification is easily the most basic and also crucial job in event-based all-natural vocabulary processing devices. Chapter 9 looks at getting a grasp on the cell-specific merging designs of transcription factors.Chapter 10 looks at what exactly is the source of our capability to understand orthographic information. Chapter 11 displays comprehending the simplest way to understand blockage at one area.

Abstract: Artificial Intelligence (AI) for security management explores terminologies of security and how AI can be applied to automate security processes. Additionally, the text provides detailed explanations and recommendations for how implement procedures. Practical examples and real-time use cases are evaluated and suggest appropriate algorithms based on the author's experiences. Threat and associated securities from the data, process, people, things (e.g., Internet of things), systems, and actions were used to develop security knowledge base, which will help readers to build their own knowledge base. This book will help the readers to start their AI journey on security and how data can be applied to drive business actions to build secure environment.

Summary: This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigms algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Summary: Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic availability of historical and real-time financial data in combination with techniques from AI and machine learning will also change the financial industry in a fundamental way. This practical book explains how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science how machine and deep learning algorithms can be applied to finance.

Summary: This unique book discusses a selection of highly relevant topics in the Social Internet of Things (SIoT), including blockchain, fog computing and data fusion. It also presents numerous SIoT-related applications in fields such as agriculture, health care, education and security, allowing researchers and industry practitioners to gain a better understanding of the Social Internet of Things.

Summary: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Summary: Formal Methods in Computer Science gives students a comprehensive introduction to formal methods and their application in software and hardware specification and verification. The first part introduces some fundamentals in formal methods, including set theory, functions, finite state machines, and regular expressions. The second part focuses on logic, a powerful formal language in specifying systems properties. It covers propositional logic, predicate logic, temporal logic, and model checking. The third part presents Petri nets, the most popular formal language in system behavior modeling. In additional to regular Petri nets, this part also examines timed Petri nets and high-level Petri nets. The textbook is ideal for undergraduate or graduate courses in computer engineering, software engineering, computer science, and information technology programs. Parts of the book are useful reading material in undergraduate computer course and as a reference guide for students researching the area of formal system specification and validation.

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: "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: "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: Learn how to make better decisions and write cleaner Ruby code. This book shows you how to avoid messy code that is hard to test and which cripples productivity. Author Carleton DiLeo shares hard-learned lessons gained from years of experience across numerous codebases both large and small. Each chapter covers the topics you need to know to make better decisions and optimize your productivity. Many books will tell you how to do something; this book will tell you why you should do it. Start writing code you love. What You Will Learn Build better classes to help promote code reuse Improve your decision making and make better, smarter choices Identify bad code and fixed it Create quality names for all of your variables, classes, and modules Write better, concise classes Improve the quality of your methods Properly use modules Clarify your Boolean logic See when and how you refactor Improve your understanding of TDD and write better tests Who This Book Is For This book is written for Ruby developers. There is no need to learn a new language or translate concepts to Ruby.

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 considers the role journalism education plays in coping with a changing media landscape. It looks at how journalists can empower themselves in an effort to excel in an evolving environment and considers whether it suffices for them to master pre-millennial basic skills or whether brand new competencies need to be incorporated. Few dramatic qualifications are spared when discussing the changes that have shaken the news environment during the noughties. Digitization has both empowered and tried professional journalists through multimedia news production, media convergence and not least a maturing commercial internet. Moreover, digitization has also influenced, and been influenced by, other societal changes such as global financial tensions, evolving multicultural societies, and emerging democracies in search for a suitable journalistic paradigm. Indeed, the rather technological evolutions emphasized time and again, cannot be detached from a cultural setting. This is why an investigation in required competencies benefits from an explicit socio-cultural and cross-continental perspective. As this book tackles a varied set of news ecosystems, it is our hope to offer a nuanced view on what indeed seems to be a global fluidity in journalism practice. Explicit emphasis is put on the role of journalism education as facilitator for, and even innovator in, required journalistic competencies. Time will tell whether or not 'news ecosystems' will again stabilize. This volume makes a number of concrete recommendations towards journalism training and discusses a number of case studies across several continents, illustrating how goals are attuned to a changed news environment. As this book links academic paradigms to concrete journalism practice and education, its reading is recommended both for practitioners and educators.

Summary: "Contemporary journalism faces a crisis of trust that threatens the institution and may imperil democracy itself. Critics and experts see a renewed commitment to local journalism as one solution. But a lasting restoration of public trust requires a different kind of local journalism than is often imagined, one that engages with and shares power among all sectors of a community. Andrea Wenzel models new practices of community-centered journalism that build trust across boundaries of politics, race, and class, and prioritize solutions while engaging the full range of local stakeholders. Informed by case studies from rural, suburban, and urban settings, Wenzel's blueprint reshapes journalism norms and creates vigorous storytelling networks between all parts of a community. Envisioning a portable, rather than scalable, process, Wenzel proposes a community-centered journalism that, once implemented, will strengthen lines of local communication, reinvigorate civic participation, and forge a trusting partnership between media and the people they cover"-- Provided by publisher.

Summary: "The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book"-- Provided by publisher.

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: Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.' Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that's both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: -Improving time-to-value with infused AI models for common use cases -Optimizing knowledge work and business processes -Utilizing AI-based business intelligence and data visualization -Establishing a data topology to support general or highly specialized needs -Successfully completing AI projects in a predictable manner -Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

Summary: "Over 70 in-depth interviews of Fintech Founders provide lessons from some of the most successful fintech entrepreneurs that will help you understand the challenges and opportunities of applying technology and collaboration to solve some key problems of the financial services industry. This book is for entrepreneurs, for people working inside of large organizations and everyone in between who is interested to learn the secrets of successful entrepreneurs. In this advice-filled resource, Rubini gathers advice that comes from a diverse range of financial services niches including financing, banking, payments, wealth management, insurance, and cryptocurrencies, to help you harness the insights of thought leaders. Those working inside the financial services industry and those interested in working in or starting up businesses in financial services will learn valuable lessons on how to take an idea forward, how to find the right business founders, how to seek funding, how to learn from initial mistakes, and how to define and reposition your business model. Rubini also inquires into the future of fintech and uncovers provoking and insightful predictions."-- Provided by publisher

Summary: Learn how to build a design system framed within the context of your specific business needs. This book guides you through the process of defining a design language that can be understood across teams, while also establishing communication strategies for how to sell your system to key stakeholders and other contributors. With a defined set of components and guidelines, designers can focus their efforts on solving user needs rather than recreating elements and reinventing solutions. You'll learn how to use an interface inventory to surface inconsistencies and inefficient solutions, as well as how to establish a component library by documenting existing patterns and creating new ones. You'll also see how the creation of self-documenting styles and components will streamline your UX process. Building Design Systems provides critical insights into how to set up a design system within your organization, measure the effectiveness of that system, and maintain it over time. You will develop the skills needed to approach your design process systematically, ensuring that your design system achieves the purpose of your organization, your product, and your team. What You'll Learn Develop communication strategies necessary to gain buy-in from key stakeholders and other teams Establish principles based on your specific needs Design, build, implement, and maintain a design system from the ground up Measure the effectiveness of your system over time Who This Book Is For All teams, large and small, seeking to unify their design language through a cohesive design system and create buy-in for design thinking within their organization; UX, visual, and interaction designers, as well as product managers and front-end developers will benefit from a systematic approach to design.

Summary: "Humans, like many other animals, are highly social species. But what exactly makes us social? How do our biological systems implement social behavior? And, in turn, how do these social processes impact our brain and biology? These are the questions that define the young field of social neuroscience, a field that combines the study of animal models and humans in order to understand the neural, hormonal, cellular, and genomic mechanisms underlying social processes and behaviors such as imitation, loneliness, empathy, and cooperation. Intended for advanced undergraduates and graduate students, this is the first textbook to provide a synthetic approach to social neuroscience. Here, students and scholars are introduced to the field by examining a growing body of evidence that shows that the nervous system cannot be understood without consideration of the social environments in which humans and many animal species live. The first three chapters introduce readers to the neurological basis for social behavior and the concept of the social brain. Chapters four through six discuss how mental states are communicated between people. And chapters seven through nine cover the neural roots of social interactions and group thought patterns. Ultimately, this book demonstrates how the brain mediates social behaviour and provides a foundational textbook for this nascent field"-- Provided by publisher.

Summary: Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment provides theoretical concepts and practical techniques of AI and its applications in cancer management, building a roadmap on how to use AI in cancer at different stages of healthcare.

Summary: "In 2007-8 the world economy started its heady journey down in to recession. The Queen herself asked "why didn't we see this coming", but it's a question that remains unanswered. A decade on and it is still not clear exactly who is responsible for the crisis. The world has experienced the long-term impact of austerity policies on its welfare system and the political landscape is completely changed. This analysis of the media that reported on this crisis and where it came from is long overdue. The media were responsible for warning the public - a role they failed in. This book provides evidence that journalism, like bankers and regulators, needs to be held accountable. The Global Financial Crisis is a starting point, but it deserves a much wider context and explanation, one this book provides for the first time. This book assesses the role financial and economics journalists should play for society, the limits and challenges they face, the reality of the situation as we see in the content that has been narrowing. It does so with some empirical evidence and interviews with some esteemed and esteemed journalists"-- Provided by publisher.

Summary: This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19. .

Summary: Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley--who happens to be both a machine learning expert and a professional ski instructor--has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the authors practical expertise in both tech and sports is an undeniable asset for your learning process. Todays data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. You will: Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science.

Summary: "Gain the skills you need to succeed in the television industry and master the production process, from shooting and producing, to editing and distribution. This new and updated 17th edition of Television Production offers a thorough and practical guide to professional TV production techniques. Learn how to anticipate and quickly overcome commonly encountered problems in television production as author Jim Owens details each role and process, including the secrets of top-grade camerawork, persuasive lighting techniques, and effective sound treatment, as well as the subtle processes of scenic design, directing, and the art of video editing. Updated throughout, containing a range of new figures and diagrams, the 17th edition of this classic text includes: - A discussion of the changing definition of 'television' and how new technology alters viewing habits; - Interviews with professionals in the industry about the challenges they face during the production process and the advice they would give to those trying to break into the production and television industries; - A review of production practices and techniques for VR; - A description of the latest cameras and equipment, including LED lighting and remote production; - Guidance and techniques for low-budget, DIY-style productions; - A comprehensive resource page for instructors, containing slides and testing materials to aid in the learning process can be found at www.routledge.com/cw/owens."-- Provided by publisher.

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: 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: "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.