Artificial Neural Networks and Deep Learning

Artificial Neural Networks is a computational model based on the structure and functions of biological neural networks. ANNs are considered nonlinear statistical data modelling tools where the complex relationships between inputs and outputs are modelled or patterns are found.

Modern Digital Computers outperform humans in the domain of numeric computation and related symbol manipulation. However, humans can effortlessly solve complex perpetual problems (like recognizing a man in a crowd from a mere glimpse of his face) at such a high speed and extend as to dwarf the world’s fastest computer. The biological neural system architecture is completely different. This difference significantly affects the type of functions each computational model can best perform.

Numerous efforts to develop intelligent programs based on von Neumann’s centralized  architecture have not resulted in general-purpose intelligent programs. Inspired by biological neural networks, ANNs are massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections. ANN models attempt to use some organizational principles believed to be used in the human.

  • Artificial Neurons
  • Speech Recognition
  • Machine Translation
  • Advances in Artificial Neural Systems
  • ANN Controller for automatic ship berthing
  • Adaptive Neuro Fuzzy Interface System
  • Deep Feed forward Networks
  • Autoencoders
  • Optimization for training deep neural network models

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