Artificial Neural Networks & Deep Learning

An Artificial Neural Network (ANN) is data organizing worldview that is forced by the way organic sensory systems. Artificial Neural Networks perform specific tasks like pattern recognition, clustering etc. on the computer. They are similar to the human brains, obtain knowledge through learning and their knowledge is stored within interneuron connection strengths. An Artificial Neural Network is designed for a particular application such as design acknowledgment or information arrangement, through a learning procedure.  They are capable of processing and modelling nonlinear dependence between inputs and outputs in parallel. They are characterized by containing flexible weights along paths between neurons that can be tuned by a learning algorithm that learns from observed data in order to improve the model. Deep Learning is a function of artificial intelligence that copies the workings of the human brain in processing data and creating designs for use in decision making. Deep learning is structured learning that is a section of the machine learning method based on learning data description. It uses some form of inclination extraction for training via back propagation. The layers used in deep learning incorporate hidden layers of artificial neural networks and sets of propositional formulas.


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