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Summary: Improving your craft is a key skill for product and user experience professionals working in the digital era. There are many established methods of product development to inspire and focus teams--Sprint, Lean, Agile, Kanban--all of which focus on solutions to customer and business problems. Enter XDPD, or Experiment-Driven Product Development--a new approach that turns the spotlight on questions to be answered, rather than on solutions. Within XDPD, discovery is a mindset, not a project phase. In Experiment-Driven Product Development, author Paul Rissen introduces a philosophy of product development that will hone your skills in discovery, research and learning. By guiding you through a practical, immediately applicable framework, you can learn to ask, and answer, questions which will supercharge your product development, making teams smarter and better at developing products and services that deliver for users and businesses alike. When applying the XDPD framework within your organization, the concept of an experiment--a structured way of asking, and answering, questions--becomes the foundation of almost everything you do, instilling a constant sense of discovery that keeps your team inspired. All types of activities, from data analysis to writing software, are seen through the lens of research. Rather than treating research as a separate task from the rest of product development, this book approaches the entire practice as one of research and continuous discovery. Designing successful experiments takes practice. Thats where Rissens years of industry expertise come in. In this book, you are given step-by-step tools to ensure that meaningful, efficient progress is made with each experiment. This approach will prove beneficial to your team, your users, and most importantly, to your products lasting success. Experiment-Driven Product Development offers a greater appreciation of the craft of experimentation and helps you adapt it in your own context. In o ur modern age of innovation, XDPD can put you ahead. Go forth and experiment!

Summary: This book focuses on some aspects of public health in the first three decades of the twentieth-century in Bombay Presidency.

Summary: "At a time when print media reigned supreme and newspapers were legion, Dorothy Thompson, John Gunther, Vincent Sheean, and Rayna Raphaelson Prohme impulsively left their homes to reinvent themselves as international journalists and adopt the power of the press as their own. In Fighting Words, acclaimed historian Nancy Cott follows these four largely unknown young Americans to reveal how foreign journalism shaped America's sense of its place in the world. Dorothy, John, Vincent, and Rayna serve as a counter to the devil-may-care jazz babies of the 1920s who scandalized their elders to no purpose beyond frivolity. Instead, the four directly confronted major political challenges that still reverberate today- democracy versus authoritarianism, global responsibility versus isolationism, press objectivity versus propaganda. They revealed the political instability that circled most of the globe as a legacy of the redrawing of world order after World War I. By the early 1930s, unlike Americans at home fixated on the Depression and New Deal, they were in the antifascist vanguard, well aware of Hitler's impending menace. At the same time, they were actively rethinking relationships between men and women. All four navigated sexual affairs and frictions, marriages and divorces. Their experiences traced the development not only of international journalism but also the making of the modern self at a time when the value of sexual freedom grated against traditional morality. A group biography of four extraordinary Americans abroad, and a paean to a golden age of journalism, Fighting Words shows how these young cosmopolitans reshaped America's sense of its own place in the world"-- Provided by publisher.

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: 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: "This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data"-- Provided by publisher.

Summary: "Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals."--

Summary: "From terrorism and public health to museums, schools, and homes, FUTUREPROOF asks us to expand our purview of security, turning our attention to how security is lived, felt, and perceived through the violences of everyday life. Editors Asher Ghertner, Daniel Goldstein, and Hudson Fann propose the modality of "security aesthetics" as a way to understand how sensory and affective experiences help regulate bodies, spaces, and states in the service of "futureproofing" society against anticipated risks and perils. Taken broadly as the domain of sense perception, aesthetics provides a framework for considering the body's interactions with the material world and the processes by which these interactions translate into subjective judgments of taste. The contributions to this volume show us how security aesthetics work to fashion differently imagined futures, illuminating the interplay of sense perception and articulated reason in the operations of security apparatuses"-- Provided by publisher.

Summary: Global media expert Dal Yong Jin examines the nexus of globalization, digital media, and contemporary popular culture in this empirically rich, student-friendly book. Offering an in-depth look at globalization processes, histories, texts, and state policies as they relate to the global media, Jin maps out the increasing role of digital platforms as they have shifted the contours of globalization. Case studies and examples focus on ubiquitous digital platforms, including Facebook, YouTube, and Netflix, in tandem with globalization so that the readers are able to apply diverse theoretical frameworks of globalization in different media milieu. Readers are taught core theoretical concepts which they should apply critically to a broad range of contemporary media policies, practices, movements, and technologies in different geographic regions of the world - North America, Europe, Africa, Latin America, and Asia - with a view to determining how they shape and are shaped by globalization. End-of-chapter discussion questions prompt further critical thinking and research. Students doing coursework in digital media, global media, international communication, and globalization will find this new textbook to be an essential introduction to how media have influenced a complex set of globalization processes in broad international and comparative contexts.

Summary: Glossaries are the useful reference sources generally referred to by the students, teachers, researches etc. in their day-to-day work. Glossaries can be very useful for helping the students, research scholars etc. to understand words from their use in reading or in class. This book covers the wide range of terms (A–Z) likely to be encountered by students of journalism and mass communication. It is an essential companion to all the students taking courses in Journalism and Mass Communication studies, as well as related subjects such as Communications Studies, Media Studies, Television and Radio Production etc. Assuming little or no prior knowledge of the subject, it covers terminology relating to the journalism and mass communication. Related topic areas are covered where they impact on or offer explanations of journalism, for example, in law and sociology. Effort has been made to incorporate as many terms as possible and cover common terms including Information and communication technology which can be very useful for those working in the area of journalism and mass communication and other related areas. This book consists of alphabetical list of terms which provide simple definitions and some illustrations for easy understanding of the terms. This book is also an indispensable reference tool for researchers, students and professionals in this area. Attempts have been made to describe the various concepts in systematic way and in a very simple and easy language. The main objective of writing this book to provide sufficient number of specialized and technical words and their definitions related to the area of journalism and mass communication. The special feature of this Glossary is that at the end a chronology of events is given in the area of journalism, mass communication, mass media and related areas.

Summary: "This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"-- Provided by publisher.

Summary: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.

Summary: Data Exploration; An overview; Feature identification; The curse of dimensionality; Feature selection and feature extraction; Pearson correlation example; Cleaning and preparing data; Handling missing data; Missing categorical data; Missing numerical data; Handling noise; Handling outliers; Transforming and normalizing data; Summary; Chapter 3: Tour of Machine Learning Algorithms; Introduction to machine learning; Types of learning; Unsupervised learning; Supervised learning; Measuring accuracy; Supervised learning algorithms; Reinforcement learning; Categories of algorithms.

Summary: A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models function Use deep learning for solving problems related to vision, image, text, and sequence applications Book Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you'll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learn Understand the key mathematical concepts for building neural network models Discover core multivariable calculus concepts Improve the performance of deep learning models using optimization techniques Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer Understand computational graphs and their importance in DL Explore the backpropagation algorithm to reduce output error Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs) Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning

Summary: Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.

Summary: "Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare"-- Provided by publisher.

Summary: This book covers challenges and solutions in establishing Industry 4.0 standards for Internet of Things. It proposes a clear view about the role of Internet of Things in establishing standards. The sensor design for industrial problem, challenges faced, and solutions are all addressed. The concept of digital twin and complexity in data analytics for predictive maintenance and fault prediction is also covered. The book is aimed at existing problems faced by the industry at present, with the goal of cost-efficiency and unmanned automation. It also concentrates on predictive maintenance and predictive failures. In addition, it includes design challenges and a survey of literature. Discusses the move towards Industry 4.0 standards and creating a digital twin concept to increase production Presents results and design solutions for industrial standards for IoT Intended for researchers, industrialists and data scientists.

Summary: The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.

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: A spotlight on the development process and contributing team members; An introduction to coding languages for interactive media; and A guide to usability in interactive media. Introduction to Interactive Digital Media will help both students and professionals understand the varied creative, technical, and collaborative skills needed in this exciting and emerging field"-- Provided by publisher.

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: "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: Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. You will: Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch.

Summary: This book addresses the major challenges in realizing smart city and sensing platforms in the era of the IoT and the Cloud. Challenges vary from cost and energy efficiency to availability and service quality. To tackle these challenges, sensors must meet certain expectations and requirements such as size constraints, manufacturing costs and resistance to environmental factors. The book focuses on both the design and implementation aspects for smart city and sensing applications that are enabled and supported by IoT paradigms. Attention is also given to data delivery approaches and performance aspects.

Summary: The age of detachment is over. Interactive, interconnected and participatory, journalism today is a constant live-stream of outrage, terror, polarised politics and fake news. With our news landscape dominated by emotionally charged material, Stephen Jukes investigates emotionality’s impact on the practice of journalism and the journalists themselves. Adopting a psychosocial approach and including interviews with top journalists, this book combines theory and practice to explore:the history of emotive journalism social media’s use of emotion to engage audiences emotion's impact on how journalists work how journalists deal with their own emotions when they are covering difficult stories Navigating the complex relationship between Journalism and Emotion through the exploration of key concepts, in-depth interviews with influential journalists and analysis of traumatic news stories, this book is a must-read for academics and social science students alike.

Summary: Journalism in Turkey has an ambivalent characteristic. On the one hand, the social demand for genuine journalism has increased, and on the other hand, news has turned into a tool within the polluted political polarization atmosphere. In the age of fake news and post-truth, practices of journalism in Turkey both contain significantly striking examples of how media professionals overcome the barriers and also give some clues about the changing nature of journalism. The book examines the deep crisis mainstream media experience in Turkey. New-born media institutions, alternatives, their start-up strategies, and transformation of journalism field are scrutinized by qualitative and quantitative methods. The book aims to present a current picture of journalism in Turkey by underlining both historical continuities and breaks from the tradition.

Summary: "This book presents an interdisciplinary approach to the scientific study of the relation between language and society, language and culture, language and mind. It integrates frameworks from sociolinguistics and linguistic anthropology and emerging strands of research on language and new media, in order to demonstrate how language undergirds human thought and social behaviors. It is designed as an introductory textbook aimed at students with little to no background in linguistics. Each chapter covers the main aspects of a particular topic or area of study, while also presenting future avenues of study. This edition includes discussions on: social media and the creation of identity; gestural communication; emoji writing; multimodality; human-computer interaction. Discussions are supported by a wealth of pedagogical features, including sidebars, as well as activities, assignments, and a glossary at the back. The overall aim is to demonstrate the dynamic connections between language, society, thought, and culture, and how they continue to evolve in today's rapidly changing digital world. It is ideal for students in introductory courses in sociolinguistics, language and culture, and linguistic anthropology"-- Provided by publisher.

Summary: Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects. If you aren't using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. You will: Get data into and out of Python code Prepare the data and its format Find the meaning of the data Visualize the data using iPython.

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: Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. You will: Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples.

Summary: Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch

Summary: This book provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. It contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. It is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: learn the languages of machine learning including Hadoop, Mahout, and Weka; understand decision trees, Bayesian networks, and artificial neural networks; implement association rule, real time, and batch learning; develop a strategic plan for safe, effective, and efficient machine learning

Summary: This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.

Summary: Machine learning and data science will significantly transform the finance industry in the next few years. With this practical guide, professionals at hedge funds, investment and retail banks, and fintech firms will learn how to build ML algorithms crucial to this industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).

Summary: "This book describes Machine Learning techniques and algorithms that have been used in recent real-world application. It provides an introduction to Machine Learning, describes the most widely used techniques and methods. It also covers Deep Learning and related areas such as function approximation or. The book gives real world examples where Machine Learning techniques are applied and describes the basic math and the commonly used learning techniques"-- Provided by publisher.

Summary: "The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice"-- Provided by publisher.

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: Research on Smart Grids has recently focused on the energy monitoring issue, with the objective of maximizing the user consumption awareness in building contexts on the one hand, and providing utilities with a detailed description of customer habits on the other. In particular, Non-Intrusive Load Monitoring (NILM), the subject of this book, represents one of the hottest topics in Smart Grid applications. NILM refers to those techniques aimed at decomposing the consumption-aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. This book provides a status report on the most promising NILM methods, with an overview of the publically available dataset on which the algorithm and experiments are based. Of the proposed methods, those based on the Hidden Markov Model (HMM) and the Deep Neural Network (DNN) are the best performing and most interesting from the future improvement point of view. One method from each category has been selected and the performance improvements achieved are described. Comparisons are made between the two reference techniques, and pros and cons are considered. In addition, performance improvements can be achieved when the reactive power component is exploited in addition to the active power consumption trace.

Summary: Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models to ensure optimum performance on previously unseen data. Book Description The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level. What you will learn Get to grips with building automated machine learning models Build classification and regression models with impressive accuracy in a short time Develop neural network classifiers with AutoML techniques Compare AutoML models with traditional, manually developed models on the same datasets Create robust, production-ready models Evaluate automated classification models based on metrics such as accuracy, recall, precision, and f1-score Get hands-on with deployment using Flask-RESTful on localhost Who this book is for Data scientists, data analysts, and software developers who are new to machine learning

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 new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of ​​machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of ​​equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.

Summary: "This book will provide in depth information about handling and managing healthcare data by Machine Learning methods. It will express the long-standing challenges in healthcare informatics and provide rational orientations on how to deal with them"-- Provided by publisher.

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: This book provides practical examples of machine learning applied to science to help researchers make an informed choice about using the method in chemistry.

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: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Summary: Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries About This Book Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications Packed with practical advice and tips to help you get to grips with applied machine learning 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. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary. What You Will Learn Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve Learn how to implement classification, regression, and clustering Develop a sustainable strategy for customer retention 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, algorithms, and the toolbox for deep learning Write your own activity recognition model for eHealth applications using mobile sensors In Detail As the amount of data 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 to quickly gain insight from complex data.