An Introduction to Deep Learning
By Ludovic Arnold, Sébastien Rebecchi, Sylvain Chevallier, Hélène Paugam-Moisy. 2011
32 citations recorded by the source corpus.
The deep learning paradigm tackles problems on which shallow architectures (e.g. SVM) are affected by the curse of dimensionality. As part of a two-stage learning scheme involving multiple layers of non-linear processing a set of statistically robust features is automatically extracted from the data. The present tutorial introducing the ESANN deep learning special session details the state-of-the-art models and summarizes the current understanding of this learning approach which is a reference for many difficult classification tasks.
Subfields: Building and Construction, Civil and Structural Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
In Civil and Structural Engineering
Citations in the corpus
- Deep learning for financial time series forecasting in A-Trader system (2017, background): “Deep Learning is a set of machine learning algorithms that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations [27, 28].”
- Sentiment Analysis Using Deep Learning Techniques: A Review (2017, methodology): “A very motivating approach in natural language processing has explored that complex multi-tasking such as semantic labeling can be highly performed by using deep architectures[13].”