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Books > Computing & IT > Applications of computing > Databases > Data mining
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Representation Learning - Propositionalization and Embeddings (Paperback, 1st ed. 2021)
Loot Price: R4,406
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Representation Learning - Propositionalization and Embeddings (Paperback, 1st ed. 2021)
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This monograph addresses advances in representation learning, a
cutting-edge research area of machine learning. Representation
learning refers to modern data transformation techniques that
convert data of different modalities and complexity, including
texts, graphs, and relations, into compact tabular representations,
which effectively capture their semantic properties and relations.
The monograph focuses on (i) propositionalization approaches,
established in relational learning and inductive logic programming,
and (ii) embedding approaches, which have gained popularity with
recent advances in deep learning. The authors establish a unifying
perspective on representation learning techniques developed in
these various areas of modern data science, enabling the reader to
understand the common underlying principles and to gain insight
using selected examples and sample Python code. The monograph
should be of interest to a wide audience, ranging from data
scientists, machine learning researchers and students to
developers, software engineers and industrial researchers
interested in hands-on AI solutions.
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