Back-propagation in machine learning

 Back-propagation in machine learning

 

Back-propagation in machine learning

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•  Back-propagation or backward propagation of errors

is a method to determine the conditions under which

errors are removed form a neural network which is

built to resemble the human's neurons functions

by changing the weights and biases of the network

continually with a goal to arrive at an actual output

which would match the target output .

 

• The neurons present in the network catch the transmitted

information and relay the information along to

the next neuron in the line . And in this process , the

entire network is built for relaying the information from

the source of information propagation to the desired

target and in this way each neuron in the network is

shared a portion of the total information relayed and

as such all neurons keep passing information to next

neuron in line until the set of neurons create a final

Output .Thr total sum of errors at the rsult / target ise calculated by the method of Back-propagation