This book features the contributions presented at the 5th International KES Conference on Smart Education and e-Learning, which took place in Gold Coast, Australia, June 20–22, 2018. The peer-reviewed papers are grouped into several interconnected parts: Part 1 – Smart Education: Systems and Technology, Part 2 – Smart Pedagogy, Part 3 – Smart Education: Case Studies and Research, and Part 4: Sustainable Learning Technologies: Smart Higher Education Futures.
Smart education and smart e-learning are emerging and rapidly growing areas with the potential to transform existing teaching strategies, learning environments, and educational activities and technology in the classroom. Smart education and smart e-learning focus on enabling instructors to develop new ways of achieving excellence in teaching in highly technological smart classrooms, and providing students with new opportunities to maximize their success and select the best options for their education, location and learning style, as well as the mode of content delivery. This book serves as a useful source of research data and valuable information on current research projects, best practices and case studies for faculty, scholars, Ph.D. students, administrators, and practitioners – all those who are interested in smart education and smart e-learning.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors, and perturbations in the automatic control of industrial production processes. It also discusses the drastic changes in financial, economic, and work processes that are currently being experienced by the computational and engineering sciences community.
Addresses key aspects, such as the integration of neural algorithms and procedures for the recognition, the analysis and detection of dynamic complex structures and the implementation of systems for discovering patterns in data, the book highlights the commonalities between computational intelligence (CI) and information and communications technologies (ICT) to promote transversal skills and sophisticated processing techniques.
This book is a valuable resource for
a. The academic research community
b. The ICT market
c. PhD students and early stage researchers
d. Companies, research institutes
e. Representatives from industry and standardization bodies
There were two main motivations in initiating the Innovation through Knowledge Transfer series. The first aim was to provide the chance for publication on a subject where few opportunities exist already. The second motivation was to foster the development of a community from the diverse range of individuals practicing knowledge transfer. It is becoming clear that the delegates of the conference are drawn from a diverse community of practice. InnovationKT’2010 has succeeded in bringing together contributions from both the academic and practitioner sections of the knowledge transfer community.
The programme contained seven invited keynote talks, 40 oral presentations grouped into eight sessions, and one interactive workshop. The proceedings contain 29 chapters drawn from this material. There were 91 registered delegates drawn from 10 countries of the world. The field of knowledge transfer is still immature, but these proceedings demonstrate that InnovationKT conference is making a significant contribution to its academic development.
Smart education and smart e-Learning are emerging and rapidly growing areas. They represent the innovative integration of smart systems, technologies and objects, smart environments, smart pedagogy, smart learning and academic analytics, various branches of computer science and computer engineering, and state-of-the-art smart educational software and/or hardware systems.
It contains a total of 48 peer-reviewed book chapters that are grouped into several parts: Part 1 – Smart Pedagogy, Part 2 – Smart e-Learning, Part 3 – Systems and Technologies for Smart Education, Part 4 – Smart Teaching, and Part 5 – Smart Education: National Initiatives and Approaches.
The book offers a valuable source of research data, information on best practices, and case studies for educators, researchers, Ph.D. students, administrators, and practitioners—and all those who are interested in innovative areas of smart education and smart e-Learning.