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Personalized Machine Learning

Personalized Machine Learning PDF Author: Julian McAuley
Publisher: Cambridge University Press
ISBN: 1316518906
Category : Computers
Languages : en
Pages : 337

Book Description
Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

Personalized Machine Learning

Personalized Machine Learning PDF Author: Julian McAuley
Publisher: Cambridge University Press
ISBN: 1316518906
Category : Computers
Languages : en
Pages : 337

Book Description
Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

Teaching Machines

Teaching Machines PDF Author: Audrey Watters
Publisher: MIT Press
ISBN: 026254606X
Category : Education
Languages : en
Pages : 325

Book Description
How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Deep Learning for Personalized Healthcare Services

Deep Learning for Personalized Healthcare Services PDF Author: Vishal Jain
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110708124
Category : Computers
Languages : en
Pages : 268

Book Description
This book uncovers the stakes and possibilities involved in realising personalised healthcare services through efficient and effective deep learning algorithms, enabling the healthcare industry to develop meaningful and cost-effective services. This requires effective understanding, application and amalgamation of deep learning with several other computing technologies, such as machine learning, data mining, and natural language processing.

Web Information Systems Engineering – WISE 2019

Web Information Systems Engineering – WISE 2019 PDF Author: Reynold Cheng
Publisher: Springer Nature
ISBN: 3030342239
Category : Computers
Languages : en
Pages : 812

Book Description
This book constitutes the proceedings of the 20th International Conference on Web Information Systems Engineering, WISE 2019, held in Hong Kong, China, in November 2019. Due to the problems/protests in Hong Kong, WISE 2019 was postponed from November 26-30, 2019 until January 19-22, 2020. The 50 full papers presented were carefully reviewed and selected from 211 submissions. The papers are organized in the following topical sections: blockchain and crowdsourcing; machine learning; deep learning; recommender systems, data mining; web-based applications; entity linkage and disambiguation; graph learning; knowledge graphs; graph mining; and text mining.

Applications of Deep Learning and Big IoT on Personalized Healthcare Services

Applications of Deep Learning and Big IoT on Personalized Healthcare Services PDF Author: Wason, Ritika
Publisher: IGI Global
ISBN: 1799821021
Category : Medical
Languages : en
Pages : 248

Book Description
Healthcare is an industry that has seen great advancements in personalized services through big data analytics. Despite the application of smart devices in the medical field, the mass volume of data that is being generated makes it challenging to correctly diagnose patients. This has led to the implementation of precise algorithms that can manage large amounts of information and successfully use smart living in medical environments. Professionals worldwide need relevant research on how to successfully implement these smart technologies within their own personalized healthcare processes. Applications of Deep Learning and Big IoT on Personalized Healthcare Services is a pivotal reference source that provides a collection of innovative research on the analytical methods and applications of smart algorithms for the personalized treatment of patients. While highlighting topics including cognitive computing, natural language processing, and supply chain optimization, this book is ideally designed for network designers, analysts, technology specialists, medical professionals, developers, researchers, academicians, and post-graduate students seeking relevant information on smart developments within individualized healthcare.

Deep Learning

Deep Learning PDF Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801

Book Description
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.

Personalized Learning

Personalized Learning PDF Author: Peggy Grant
Publisher: International Society for Technology in Education
ISBN: 1564845443
Category : Education
Languages : en
Pages : 200

Book Description
Personalized Learning: A Guide for Engaging Students with Technology is designed to help educators make sense of the shifting landscape in modern education. While changes may pose significant challenges, they also offer countless opportunities to engage students in meaningful ways to improve their learning outcomes. Personalized learning is the key to engaging students, as teachers are leading the way toward making learning as relevant, rigorous, and meaningful inside school as outside and what kids do outside school: connecting and sharing online, and engaging in virtual communities of their own Renowned author of the Heck: Where the Bad Kids Go series, Dale Basye, and award winning educator Peggy Grant, provide a go-to tool available to every teacher today—technology as a way to ‘personalize’ the education experience for every student, enabling students to learn at their various paces and in the way most appropriate to their learning styles.

Deep Learning for Personalized Healthcare Services

Deep Learning for Personalized Healthcare Services PDF Author: Vishal Jain
Publisher: de Gruyter
ISBN: 9783110708004
Category : Computers
Languages : en
Pages : 0

Book Description
This book uncovers the stakes and possibilities involved in realising personalised healthcare services through efficient and effective deep learning algorithms, enabling the healthcare industry to develop meaningful and cost-effective services. This

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Fundamentals of Machine Learning for Predictive Data Analytics, second edition PDF Author: John D. Kelleher
Publisher: MIT Press
ISBN: 0262361108
Category : Computers
Languages : en
Pages : 853

Book Description
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. 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. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Introduction to Machine Learning

Introduction to Machine Learning PDF Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262028182
Category : Computers
Languages : en
Pages : 639

Book Description
Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.