Artificial Neural Networks Will Change The Future

What if i tell you that it is possible for a computer to recognize objects as us humans do! Yes, this is possible with ANN which is vaguely inspired by biological neural systems. Such systems  learn tasks by considering examples generally without task specific programming.

As an example they learn to identify images of a dog by analyzing examples that has been labeled as 'dog' or 'not dog' and using the results to identify dogs in other images. They evolve their own set of relevant characteristics from the learning material they process.

Basically there are 3 layers in an ANN;

• Input layer 
• Hidden layer
• Output layer

3 layers of ANN

• Input Layer
• The Input layer communicates with the external environment that presents a pattern to the neural network. 
• Its job is to deal with all the inputs only. 
• This input gets transferred to the hidden layers which are explained below. 
• The input layer should represent the condition for which we are training the neural network. 
• Every input neuron should represent some independent variable that has an influence over the output of the neural network 

• Hidden Layer
• The hidden layer is the collection of neurons which has activation function. 
• Its job is to process the inputs obtained by its previous layer. So it is the layer which is responsible extracting the required features from the input data. 
• we can use 3 to 5 hidden layers based on the degree of complexity of the problem or the degree of accuracy required. 

• Output Layer

• The output layer of the neural network collects and transmits the information. 
• The pattern presented by the output layer can be directly traced back to the input layer.
• To determine the number of neurons in the output layer, first consider the intended use of the neural network.

• How Do Neural Networks Differ From Conventional Computing?

• To better understand artificial neural computing it is important to know first how a conventional 'serial' computer and it's software process information. 

• A serial computer has a central processor that can address an array of memory locations where data and instructions are stored.

• Computations are made by the processor reading an instruction from memory addresses. 

• In a serial system the computational steps are deterministic, sequential and logical.

• In comparison,

• ANNs are not sequential or necessarily deterministic. 

• There are no complex central processors, rather there are many simple ones. 

• ANNs do not execute programmed instructions; they respond in parallel (either simulated or actual). 

• There are also no separate memory addresses for storing data. Instead, information is contained in the overall activation 'state' of the network.

This is a basic introduction about an ANN. Further more will be discussed later. Thank you!

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