Competitive Learning: Fundamentals and Applications for Reinforcement Learning through Competition
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What Is Competitive Learning
In artificial neural networks, competitive learning is a type of unsupervised learning in which nodes fight for the right to respond to a subset of the input data. This type of learning is known as "competitive learning." Competitive learning is a form of learning that is similar to Hebbian learning. It operates by raising the level of specialization at each node in the network. It works quite well for discovering clusters hidden within data.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Competitive Learning
Chapter 2: Self-organizing map
Chapter 3: Perceptron
Chapter 4: Unsupervised Learning
Chapter 5: Hebbian Theory
Chapter 6: Backpropagation
Chapter 7: Multilayer Perceptron
Chapter 8: Learning Rule
Chapter 9: Feature Learning
Chapter 10: Types of artificial neural networks
(II) Answering the public top questions about competitive learning.
(III) Real world examples for the usage of competitive learning in many fields.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of competitive learning.
What Is Artificial Intelligence Series
The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
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Apie autorių
Fouad Sabry is the former Regional Head of Business Development for Applications at HP in Southern Europe, Middle East, and Africa (SEMEA). Fouad has received his B.Sc. of Computer Systems and Automatic Control in 1996, dual masterâs degrees from University of Melbourne (UoM) in Australia, Master of Business Administration (MBA) in 2008, and Master of Management in Information Technology (MMIT) in 2010.
Fouad has more than 20 years of experience in Information Technology and Telecommunications fields, working in local, regional, and international companies, such as Vodafone and IBM in Middle East and Africa (MEA) region. Fouad joined HP Middle East (ME), based in Dubai, United Arab Emirates (UAE) in 2013 and helped develop the software business in tens of markets across Southern Europe, Middle East, and Africa (SEMEA) regions. Currently, Fouad is an entrepreneur, author, futurist, focused on Emerging Technologies, and Industry Solutions, and founder of One Billion Knowledgeable (1BK) Initiative.
