**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

**P**neurons consists of the following components:

_{j}(t)

an

*activation***a**depend on discrete parameter_{j }(t),

*a threshold*

*Î¸*_{j}**,**it keeps fixed unless changed by a learning function

an activation function

**F**which computes the new activation at a given time**and net input***t + 1 from a*_{j }(t), Î¸j**give rise to relation is:-***P*_{j }(t)

*a*_{j }(t+1) = f{a_{j }(t), P_{j }(t), Î¸_{j}} .

an
output function

**computing***F*_{out}**the output from the activation**

*o*_{j }(t) = F_{out }{a_{j }(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

**connected to input**

*i***Each network assigned a weight**

*j*.

*w*_{i}_{j}.

**Propagation function :-**This function compute the input

**from the neuron**

*P*_{j }(t)**o**. so,

_{i }(t)*is the successor of*

**j**

*i**and*

**i****is the predecessor of**

*j*.**∑**

*P*_{j }(t) =

*o*_{j }(t)

*w*_{ij}

_{}
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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