Problem solving in computing is referred to as computational thinking. The theory behind this concept is challenging in its technicalities, yet simple in its ideas. This book introduces the theory of computation from its inception to current form of complexity; from explanations of how the field of computer science was formed using classical ideas in mathematics by Gödel, to conceptualization of the Turing Machine, to its more recent innovations in quantum computation, hypercomputation, vague computing and natural computing. It describes the impact of these in relation to academia, business and wider society, providing a sound theoretical basis for its practical application.Written for accessibility, Demystifying Computation provides the basic knowledge needed for non-experts in the field, undergraduate computer scientists and students of information and communication technology and software development.

The revolutions that Gregory Chaitin brought within the fields of science are well known. From his discovery of algorithmic information complexity to his work on Gödel's theorem, he has contributed deeply and expansively to such diverse fields.This book attempts to bring together a collection of articles written by his colleagues, collaborators and friends to celebrate his work in a festschrift. It encompasses various aspects of the scientific work that Chaitin has accomplished over the years. Topics range from philosophy to biology, from foundations of mathematics to physics, from logic to computer science, and all other areas Chaitin has worked on.It also includes sketches of his personality with the help of biographical accounts in some unconventional articles that will provide a rare glimpse into the personal life and nature of Chaitin.Compared to the other books that exist along a similar vein, this book stands out primarily due to its highly interdisciplinary nature and its scope that will attract readers into Chaitin's world.

The rise of intelligence and computation within technology has created an eruption of potential applications in numerous professional industries. Techniques such as data analysis, cloud computing, machine learning, and others have altered the traditional processes of various disciplines including healthcare, economics, transportation, and politics. Information technology in today’s world is beginning to uncover opportunities for experts in these fields that they are not yet aware of. The exposure of specific instances in which these devices are being implemented will assist other specialists in how to successfully utilize these transformative tools with the appropriate amount of discretion, safety, and awareness. Considering the level of diverse uses and practices throughout the globe, the fifth edition of the Encyclopedia of Information Science and Technology series continues the enduring legacy set forth by its predecessors as a premier reference that contributes the most cutting-edge concepts and methodologies to the research community. The Encyclopedia of Information Science and Technology, Fifth Edition is a three-volume set that includes 136 original and previously unpublished research chapters that present multidisciplinary research and expert insights into new methods and processes for understanding modern technological tools and their applications as well as emerging theories and ethical controversies surrounding the field of information science. Highlighting a wide range of topics such as natural language processing, decision support systems, and electronic government, this book offers strategies for implementing smart devices and analytics into various professional disciplines. The techniques discussed in this publication are ideal for IT professionals, developers, computer scientists, practitioners, managers, policymakers, engineers, data analysts, and programmers seeking to understand the latest developments within this field and who are looking to apply new tools and policies in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to software engineering, cybersecurity, information technology, media and communications, urban planning, computer science, healthcare, economics, environmental science, data management, and political science will benefit from the extensive knowledge compiled within this publication.

Geometric Computation: Foundations for Design describes the mathematical and computational concepts that are central to the practical application of design computation in a manner tailored to the visual designer. Uniquely pairing key topics in code and geometry, this book develops the two key faculties required by designers that seek to integrate computation into their creative practice: an understanding of the structure of code in object-oriented programming, and a proficiency in the fundamental geometric constructs that underlie much of the computational media in visual design.

Use the power of deep learning with Python to build and deploy intelligent web applications Key FeaturesCreate next-generation intelligent web applications using Python libraries such as Flask and DjangoImplement deep learning algorithms and techniques for performing smart web automationIntegrate neural network architectures to create powerful full-stack web applicationsBook Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learnExplore deep learning models and implement them in your browserDesign a smart web-based client using Django and FlaskWork with different Python-based APIs for performing deep learning tasksImplement popular neural network models with TensorFlow.jsDesign and build deep web services on the cloud using deep learningGet familiar with the standard workflow of taking deep learning models into productionWho this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.

DEMYSTIFYING DEEP LEARNING Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere—on the news, in think tanks, and occupies government policy makers all over the world —and ANNs often provide the backbone for AI. Relying on an informal and succinct approach, Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to natural language processing, image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning readers will also find: A volume that emphasizes the importance of classification Discussion of why ANN libraries, such as Tensor Flow and Pytorch, are written in C++ rather than Python Each chapter concludes with a “Projects” page to promote students experimenting with real code A supporting library of software to accompany the book at https://github.com/nom-de-guerre/RANT An approachable explanation of how generative AI, such as generative adversarial networks (GAN), really work. An accessible motivation and elucidation of how transformers, the basis of large language models (LLM) such as ChatGPT, work. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work. It is also a helpful text for advanced undergraduates to get a solid grounding on the topic.

This book presents an emerging new vision of the brain, which is essentially expressed in computational terms, for non-experts. As such, it presents the fundamental concepts of neuroscience in simple language, without overwhelming non-biologists with excessive biological jargon. In addition, the book presents a novel computational perspective on the brain for biologists, without resorting to complex mathematical equations. It addresses a comprehensive range of topics, starting with the history of neuroscience, the function of the individual neuron, the various kinds of neural network models that can explain diverse neural phenomena, sensory-motor function, language, emotions, and concluding with the latest theories on consciousness. The book offers readers a panoramic introduction to the “new brain” and a valuable resource for interdisciplinary researchers looking to gatecrash the world of neuroscience.

Up-to-date, hands-on introduction to computable general equilibrium models, also serving as a desk reference for experienced CGE modelers.

This engaging and accessible text addresses the fundamental question: What Is Computer Science? The book showcases a set of representative concepts broadly connected by the theme of information security, for which the presentation of each topic can be treated as a "mini" lecture course, demonstrating how it allows us to solve real problems, as well as how it relates to other subjects. The discussions are further supported by numerous examples and practical hands-on exercises. Features: presents a concise introduction to the study of algorithms and describes how computers work; introduces the concepts of data compression, and error detection and correction; highlights the role of data structures; explores the topic of web-search; reviews both historic and modern cryptographic schemes, examines how a physical system can leak information and discusses the idea of randomness; investigates the science of steganography; provides additional supplementary material at an associated website.

Much has been written about Millennials, but until now their growing presence in the field of architecture has not been examined in-depth. In an era of significant challenges stemming from explosive population growth, climate change, and the density of cities, Millennials in Architecture embraces the digitally savvy disruptors who are joining the field at a crucial time, as it grapples with the best ways to respond to a changing physical world. Taking a clear-eyed look at the new generation in the context of the design professions, Darius Sollohub begins by situating Millennials in a line of generations stretching back to early Modernism, exploring how each generation negotiates the ones before and after. He then considers the present moment, closely evaluating the significance of Millennial behaviors and characteristics (from civic-mindedness to collaboration, and time management in a 24/7 culture), all underpinned by fluency in the digital world. The book concludes with an assessment of the profound changes and opportunities that Millennial disruption will bring to education, licensure, and firm management. Encouraging new alliances, Millennials in Architecture is an essential resource for the architectural community and its stakeholders.