An Introduction to Deep Morphological Networks

An introduction to morphological neural networks

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the l...

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

An Introduction to Sociolinguistics

An Introduction to Deep Learning

Modeling data via artificial neural networks (ANN) is not a new concept. Most of the underlying techniques have been known since the 1940s. It has to be pointed out though that a series of recent advances in how the networks are trained and utilized form the foundation of today's Deep Learning ecosystem. It is a fair statement that the recent advances brought forward by Deep Learning reflect a ...

An Introduction to Deep Learning

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 nonlinear 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-th...