Recent Trends in Learning From Data

Beschreibung

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.
This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research. 
Gathers tutorials from the 2019 INNS Big Data and Deep Learning Conference Describes cutting-edge AI-based tools and applications Offers essential guidance on the design and analysis of advanced AI-based models Blends mathematical foundations and practice-oriented insights

Autor*in

Luca Oneto

Themen in »Recent Trends in Learning From Data«

Deep Learning for Graphs Feedforward neural networks Applications of tensor decomposition Continual learning models Learned data structures Causal bayesian networks Online continual learning Deep learning models Sparsity of shallow networks Growing neural networks Searching problems Learned indexes Random weights Kruskal tensor Tucker form with multilinear rank

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