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Representation Learning: Propositionalization and Embeddings

By (author) Nada LavraÄ, By (author) Vid PodpeÄan, By (author) Marko Robnik-Å ikonja

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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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The Order of Time

Carlo Rovelli
Paperback / softback
224 pages
was: R 295.95
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Available from overseas. Usually dispatched in 3 to 6 weeks

Originally published in Italian: L'ordine del tempo (Milan: Adelphi Edizioni, 2017).

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The Coming Wave: AI, Power and Our Future

Mustafa Suleyman
Paperback / softback
352 pages
was: R 295.95
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Helgoland: The Strange and Beautiful Story of Quantum Physics

Carlo Rovelli
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208 pages
was: R 295.95
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