BACK PROPAGATION LEARNING THEORY1

Submitted to Presented bySubmitted to Presented by

Dr. Vishal Sahani Puneet Kr Singh

M.Tech(2nd sem)

Full Time

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BACKPROPAGATION NEURAL NETWORK

One of the most commonly used supervised ANN model is back propagation network that uses back propagation learning algorithm.

These elements or nodes are arranged into different layers: input, middle and output.

The output from a back propagation neural network is computed using a procedure known as the forward pass

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The input layer propagates a particular input vector’s components to each node in the middle layer.

Middle Layer nodes compute output values, which becomes input to the nodes of output layer.becomes input to the nodes of output layer.

The output layer nodes compute the network output for the particular input vector.

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Multi-layer neural network using

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Multi-layer neural network using back propagation

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Artificial Neuron:Classical Activation Functions

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Linear activation Logistic activation

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Threshold activation Hyperbolic tangent activation

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if z

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Xk

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BACKPROPAGATION WEIGHT UPDATE PROCEDURE

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BACKPROPAGATION WEIGHT UPDATE PROCEDURE

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FUNCTION APPROXIMATION

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REPRESENTATIONSTEP

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Thanks For YourAttention!!!

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