Artificial
neural network
Artificial
neural network is an interconnected group of nodes. it is one of the main tools used in machine learning algorithm.
“neural” name suggested from the brain system . interconnected group of units
or nodes represents an artificial linked neurons, blue lines represents in the given figure below the
connection from the output of one artificial neuron to the input of another.
Let the
input is called I1,I2,I3 and the hidden as H1,H2,H3,H4,H5,H6 and output as
O and W(I1H1) is the weight of linkage between I1 and H1 nodes.
Following
frameworks in which ANN(Artificial Neural Network) depends---
• Using the inputs and the (Input ->Hidden node) linkages find the activation rate of Hidden Nodes
• Find the error rate at the output node and recalibrate all the linkages between Hidden Nodes and Output Nodes
• Repeat the process till
the convergence criterion is met
Warren
Sturgis McCulloch and Walter Harry Pitts created a computational model for neural networks based on algorithms
and mathematics in 1943.This Algorithms
called threshold logic. This
neural network divided into two approaches. One approach focused on biological
process in the brain while another approach focused on artificial intelligence
neural network. Neural networks require data
to learn.
Components of an artificial neural network :-
A
neural with label j receiving an input Pj (t) neurons consists of
the following components:
an activation aj (t), depend on discrete
parameter
a threshold θj , it keeps fixed
unless changed by a learning function
an activation function F which computes the new activation at a given time t + 1
from aj (t), θj and net input Pj (t) give
rise to relation is:-
aj
(t+1) = f{aj (t), Pj (t), θj} .
an
output function Fout computing
the output from the activation
oj
(t) = Fout {aj (t)}.
Output function
is known as identify function
Input neuron
serves as input interface for the whole network and the output neuron
serve as output interface of the whole network.
Connections:- the network consist
of connection each and every neuron output i connected to input j
. Each network assigned a weight wij.
Propagation function :- This function compute
the input Pj (t) from
the neuron oi (t). so, j is the successor of i and i is the predecessor of j.
Pj (t)
= ∑ oj (t) wij
i
Learning rule :- rule of
learning algorithm which modify the parameter of neural network. The learning
technique is to modifying the weights and thresholds of the variables within
the network.
Different types
of neural network :-
1. feedforward
neural network
2. recurrent
neural network
3. convolutional
neural networks
4. Boltzmann
machine networks
5. Hopfield
networks
Task that
can neural network perform :-
1.Gnerating CGI
(Computer
Generated Imagery) faces
2. Machine
translation
3.making
car drive automatically on the road
4.Reading
our minds
5.Fraud
detection
And some
other tasks which can neural network perform perfectly
god
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