Explain Neural Networks.

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 Neural Networks

Neural Network is a set of connected INPUT/OUTPUT UNITS, where each connection has a WEIGHT associated with it. It is a case of SUPERVISED, INDUCTIVE, or CLASSIFICATION learning.

Neural Network learns by adjusting the weights so as to be able to correctly classify the training data and hence, after the testing phase, to classify unknown data. Neural Network needs long time for training. Neural Network has a high tolerance to noisy and incomplete data.


Network Training

The ultimate objective of the training is to obtain a set of weights that makes almost all the tuples in the training data classified correctly.

Steps

  • Initialize weights with random values
  • Feed the input tuples into the network one by one
  • For each unit

- Compute the net input to the unit as a linear combination of all the inputs to the unit

- Compute the output value using the activation function

- Compute the error

- Update the weights and the bias





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