Encyclopedia of Machine Learning

Encyclopedia of Machine Learning
Author: Claude Sammut
Publisher: Springer Science & Business Media
Total Pages: 1061
Release: 2011-03-28
Genre: Computers
ISBN: 0387307680

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Encyclopedia of the Sciences of Learning

Encyclopedia of the Sciences of Learning
Author: Norbert M. Seel
Publisher: Springer Science & Business Media
Total Pages: 3643
Release: 2011-10-05
Genre: Education
ISBN: 1441914277

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.

Encyclopedia of Data Science and Machine Learning

Encyclopedia of Data Science and Machine Learning
Author: Wang, John
Publisher: IGI Global
Total Pages: 3296
Release: 2023-01-20
Genre: Computers
ISBN: 1799892212

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Encyclopedia of Artificial Intelligence

Encyclopedia of Artificial Intelligence
Author: Juan Ramon Rabunal
Publisher: IGI Global
Total Pages: 1640
Release: 2009-01-01
Genre: Computers
ISBN: 1599048507

"This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.

Encyclopedia of Information Science and Technology

Encyclopedia of Information Science and Technology
Author: Mehdi Khosrow-Pour
Publisher: IGI Global Snippet
Total Pages: 4292
Release: 2009
Genre: Computers
ISBN: 9781605660264

"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.

Machine Learning Engineering

Machine Learning Engineering
Author: Andriy Burkov
Publisher: True Positive Incorporated
Total Pages: 302
Release: 2020-09-08
Genre:
ISBN: 9781777005467

The most comprehensive book on the engineering aspects of building reliable AI systems. "If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." -Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." -Karolis Urbonas, Head of Machine Learning and Science at Amazon

Deep Learning

Deep Learning
Author: Ian Goodfellow
Publisher: MIT Press
Total Pages: 801
Release: 2016-11-10
Genre: Computers
ISBN: 0262337371

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.

Encyclopedia of Biometrics

Encyclopedia of Biometrics
Author: Stan Z. Li
Publisher: Springer Science & Business Media
Total Pages: 1466
Release: 2009-08-27
Genre: Computers
ISBN: 0387730028

With an A–Z format, this encyclopedia provides easy access to relevant information on all aspects of biometrics. It features approximately 250 overview entries and 800 definitional entries. Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications, and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information.

Encyclopedia of Artificial Intelligence

Encyclopedia of Artificial Intelligence
Author: Stuart C. Shapiro
Publisher: Wiley
Total Pages: 840
Release: 1992-01-31
Genre: Computers
ISBN: 9780471503071

Written by 350 specialists in academia, industry and government and covering all fields encompassed by artificial intelligence, this encyclopaedia has been updated and expanded to include developments in the fields of neural networks, fuzzy logic, vision and languages.

Machine Learning: Concepts, Methodologies, Tools and Applications

Machine Learning: Concepts, Methodologies, Tools and Applications
Author: Management Association, Information Resources
Publisher: IGI Global
Total Pages: 2174
Release: 2011-07-31
Genre: Computers
ISBN: 1609608194

"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe