Download Free New Perspectives Computer Concepts 2018 Book in PDF and EPUB Free Download. You can read online New Perspectives Computer Concepts 2018 and write the review.

Technology impacts every aspect of life and choices are endless. As a college student, you need to know how to evaluate devices, choose apps, maintain a compelling online reputation, and lock down digital security. NEW PERSPECTIVES ON COMPUTER CONCEPTS 2018, INTRODUCTORY goes beyond the intuitive "how-to" of apps and social media to delve into the broad concepts that are guiding current technologies such as self-driving cars, virtual reality, file sharing torrents, encrypted communications, photo forensics, and the Internet of Things. Numerous illustrations and interactive features in this complete book make mastering technical topics a breeze with a learning path that is structured with you, today's busy student, in mind. This edition offers an insightful overview of what every college student should know about using technology to complete your education, launch a successful career, and engage in issues that shape today's world.
This book describes cloud computing as a service that is "highly scalable" and operates in "a resilient environment". The authors emphasize architectural layers and models - but also business and security factors.
This book provides readers with an overview of Cloud Computing, starting with historical background on mainframe computers and early networking protocols, leading to current concerns such as hardware and systems security, performance, emerging areas of IoT, Edge Computing etc. Readers will benefit from the in-depth discussion of cloud computing usage and the underlying architecture, with focus on best practices for using a dynamic cloud infrastructure, cloud operations management and cloud security. The authors explain carefully the “why’s and how’s” of Cloud Computing, so engineers will find this book and invaluable introduction to the topic.
This book discusses the latest developments in the field of open data. The opening of data by public organizations has the potential to improve the public sector, inspire business innovation, and establish transparency. With this potential comes unique challenges; these developments impact the operation of governments as well as their relationship with private sector enterprises and society. Changes at the technical, organizational, managerial, and political level are taking place, which, in turn, impact policy-making and traditional institutional structures. This book contributes to the systematic analysis and publication of cutting-edge methods, tools, and approaches for more efficient data sharing policies, practices, and further research. Topics discussed include an introduction to open data, the open data landscape, the open data life cycle, open data policies, organizational issues, interoperability, infrastructure, business models, open data portal evaluation, and research directions, best practices, and guidelines. Written to address different perspectives, this book will be of equal interest to students and researchers, ICT industry staff, practitioners, policy makers and public servants.
"This set of books represents a detailed compendium of authoritative, research-based entries that define the contemporary state of knowledge on technology"--Provided by publisher.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
This guide to information science and technology presents a clear, concise, and approachable account of the fundamental issues, with appropriate historical and theoretical background. Topics covered include information needs, seeking, and use; representation and organization of infomation; computers and networks; structured information systems; information systems applications; users' perpectives in information systems; social informatics; communication using information technologies; information policy; and the information professions.
Softbound - New, softbound print book.
Communication and artificial intelligence (AI) are closely related. It is communication – particularly interpersonal conversational interaction – that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic.