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Nature, 2015 Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.
Yann Lecun +2 more
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2019
The chapter is devoted at illustrating the basic principles and the current results which characterize the research on Deep Learning. The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks.
Massimo Guarascio +2 more
+5 more sources
The chapter is devoted at illustrating the basic principles and the current results which characterize the research on Deep Learning. The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks.
Massimo Guarascio +2 more
+5 more sources
American Journal of Orthodontics and Dentofacial Orthopedics
Flemish Government under the "Onder-zoeksprogramma ...
Axel-Jan Rousseau +3 more
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Flemish Government under the "Onder-zoeksprogramma ...
Axel-Jan Rousseau +3 more
+7 more sources
Deep Questioning and Deep Learning
Academic Radiology, 2012T here is relatively strong empirical evidence that an effective means of enhancing learner performance is to pose ‘‘deep questions’’ (1). Examples of deep questioning include asking learners to explore the causes of a sequence of events, the motivations of the people involved, or the quality of the evidence behind a particular practice or theory.
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2023
This chapter introduces deep learning (DL) in the framework of experimentalism, taking inspiration from Pierre Oleron’s explanation of human intellectual activities in terms of long (or, deep) circuits. A history of DL is presented, from its origin in the mid-twentieth century to the breakthrough of deep neural networks (DNNs) in the last decades ...
M. Gori, F. Precioso, E. Trentin
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This chapter introduces deep learning (DL) in the framework of experimentalism, taking inspiration from Pierre Oleron’s explanation of human intellectual activities in terms of long (or, deep) circuits. A history of DL is presented, from its origin in the mid-twentieth century to the breakthrough of deep neural networks (DNNs) in the last decades ...
M. Gori, F. Precioso, E. Trentin
openaire +3 more sources
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
Building intelligent systems that are capable of extracting high-level representations from high-dimensional data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep
openaire +2 more sources
Building intelligent systems that are capable of extracting high-level representations from high-dimensional data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep
openaire +2 more sources
Deep Eutectic Solvents: A Review of Fundamentals and Applications
Chemical Reviews, 2021Brian Chen, Yong Zhang, Jeffrey M Klein
exaly