Neural Network is a very effective human brain algorithm to lead with the science fiction by the maths using a specific machine.

Neural Networks Tutorial

The neural network is to refer to science, engineering, and mathematics. Because that’s to be the target of the human brain in the machine.

So, There’s require of science about “How Human Brain Conduct”, engineering about “How To Understand To Machine Of Brain”, And mathematic about “What Is The Best Solution For Human Brain To Machine”.

The human brain consisted of an estimated 10 billion (10) neurons (nerve cells). It’s communicated through electrical signals in a specific machine. That’s impulse spikes in the voltage of the cell.

An interneuron connection is mediated by electronic junction called “synapses”, which are located on branches of the cell. Each neuron typically receives many thousands of connections from other neurons. Therefore, constantly receiving a multitude of incoming signals in the machine.

The neurons eventually reach the cell in the machine. they are integrated summed together. where the resulting signal exceeds then the neuron’s generated voltage impulse in response. Then, the responded signal transmitted to other neurons.

You can follow here Algorithm For Neural Network

Neural Network Example

where input signal are followed as x1, x2, x3…….xn, and weight signal are w1, w2, w3……..wn. Then, the output shows a step as x1w1, w1x2, x2w3…..xnwn. after the result displayed on the threshold logic unit. the positive and negative value is accessed when the threshold value such as a>0, then output show 1 and a<0, then output show 0. This is called a neural network activation function. see more here about neural network activation function.

Artificial Neural Network (ANN)

The ANN is the thought of simplified models of the neural network. that occurs in the machine as a human brain. from the biological viewpoint, the essential requirement for the neural network is that’s to attempt to capture what we believe are the essential information.

In the engineering category, the simplest artificial neuron is the threshold logic unit (TLU). It’s a basic operation is to perform a weighted sum of its input and then output. if the sum exceeds the threshold. The threshold logic unit is supposed to model the basic mechanism of real neurons.

The artificial equivalents of biological neurons are the nodes or units. synapses are modeled by a single number or weight before being sent to the equivalent of the cell in the machine. So, each input is multiplied by the weight. the weighted signals are summed together by simple arithmetic addition to supply to nodes for activation.

After activation, that’s nodes compared with the threshold. if the activation exceeds the threshold. the unit procedure high valued output 1. otherwise, output zero.

You can follow these example as given below

For example: suppose that we consider input signal x1, x2, x3…., xn. Each synapse is encapsulated by simply multiplying the incoming signal with a weight value where inhibitory actions are modeled using positive and negative values respectively.

we have n input signal w1, w2, w3….., wn. So, result signal having x1w1, x2w2, x3w3, x3w3…….xnwn. The result displayed in the analog signal. That may be positive or negative, depending on the sign of weight.
θ=x1w1+w1x2+x2w2+w2x3+…….+xnwn

As an example, consider first two input (0.5, 1.0). So, the x1=0.5 and x2=1.0. that’s why, w1=0.5 and w2=x2+w1, =0.5+1.0, =1.5. this is presented as
θ={0.5, 1.5, 2.5,……..}

To emulate the generation of action potential we need a threshold value “θ”. The ANN consisted of a neural network and TLU (Threshold Logic Unit).

Threshold Training For Weight

To place the adaption of the threshold on the same footing as the weights. there is a mathematical trick we can apply to make it look a weight.

Thus, we normally write w.x>0 as the condition for the output ‘1’. and w.x<0 for the output ‘0’ and making the explicit results in the form.

Therefore, we may think of the threshold as an extra weight that is driven by an input constantly tied to the value -1. this leads to the negative of the threshold being referred to some time as the bias.

The weight vector, which was initially of dimension n for n-input units. becomes vector w1, w2, ….. wn.

we shall call this augmented weight vector, in contexts where confusion might arise, although this terminology is by no means standard. then for all TLU, we may express the node function as follows.

w.x > 0 = y=1
w.x < 0 = y=0

putting w.x=0 now defines the decision hyperplane which according to the discussion in orthogonal to the weight vector. the zero threshold condition in the argument means that the hyperplane passes through the origin. since this is the only way that allows w.x=0

Perception

This is an enhancement of the TLU (Threshold Logic Unit) introduced by Rosenblatt in 1962. The Association unit assigned the input signal. The preception is used to mean how we defined of TLU. However, a perceptron always performs a linear separation concerning the output of A-units. its function of the input space may not be linearly separable if the A-units are non-trivial.

Google FAQ

What Is Neural Network Used For?

It is the Best Human Brain Algorithm In Machine Learning. that’s important to define neural by ANN using Threshold Logic Unit Strategy. It all depended on TLU (How We Exactly Defined In Machine Learning).

How Does a Neural Network Work?

Neural Network Algorithm depended on input, weight, output and threshold strategy. the weight takes input and compares to output. which result display on the threshold. the threshold exactly matched result show in machine learning to define neuron.

What Is Neural Network Learning?

It is not more than the Threshold Logic Unit Strategy that helps the neurons to find out the location of each neuron in machine learning.

Conclusion

It is the best algorithm to find out the solution of the human brain in the machine. that’s conduct as a human brain in machine processing.

That’s a help to build a large number of neurons in the machine. It consisted of thousands of neurons connected to machine processing.

The ANN help to the NN to accurate processing by using the Threshold Logic Unit strategy. The TLU accessed through the output and signed positive and negative values like 1 and 0.

The ANN can be displayed in linear regression or nonlinear regression format according to the going result. That is the long term process in the machine to learn about it. you can follow here more Neural Network In AI

To defined the ANN algorithm is the toughest. Because there requires a large amount of memory storage. Also, very important the electrical signal well arranged in the machine.