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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Lifelong Machine Learning, Second Edition (Paperback, 2nd Revised edition)
Loot Price: R1,818
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Lifelong Machine Learning, Second Edition (Paperback, 2nd Revised edition)
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
Expected to ship within 10 - 15 working days
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Lifelong Machine Learning, Second Edition is an introduction to an
advanced machine learning paradigm that continuously learns by
accumulating past knowledge that it then uses in future learning
and problem solving. In contrast, the current dominant machine
learning paradigm learns in isolation: given a training dataset, it
runs a machine learning algorithm on the dataset to produce a model
that is then used in its intended application. It makes no attempt
to retain the learned knowledge and use it in subsequent learning.
Unlike this isolated system, humans learn effectively with only a
few examples precisely because our learning is very
knowledge-driven: the knowledge learned in the past helps us learn
new things with little data or effort. Lifelong learning aims to
emulate this capability, because without it, an AI system cannot be
considered truly intelligent. Research in lifelong learning has
developed significantly in the relatively short time since the
first edition of this book was published. The purpose of this
second edition is to expand the definition of lifelong learning,
update the content of several chapters, and add a new chapter about
continual learning in deep neural networks-which has been actively
researched over the past two or three years. A few chapters have
also been reorganized to make each of them more coherent for the
reader. Moreover, the authors want to propose a unified framework
for the research area. Currently, there are several research topics
in machine learning that are closely related to lifelong
learning-most notably, multi-task learning, transfer learning, and
meta-learning-because they also employ the idea of knowledge
sharing and transfer. This book brings all these topics under one
roof and discusses their similarities and differences. Its goal is
to introduce this emerging machine learning paradigm and present a
comprehensive survey and review of the important research results
and latest ideas in the area. This book is thus suitable for
students, researchers, and practitioners who are interested in
machine learning, data mining, natural language processing, or
pattern recognition. Lecturers can readily use the book for courses
in any of these related fields.
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