Architecture of Artificial Neural Network

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Neural Network is a series of algorithms that are trying to mimic the human brain and find the relationship between the sets of data. It is being use in various use-cases like in regression, classification, Image Recognition and many more.

As we have talked above that neural networks tries to mimic the human brain then there might be the difference as well as the similarity between them.

Some major differences between them are biological neural network does parallel processing whereas the Artificial neural network does series processing also in the former one processing is slower (in millisecond) while in the latter one processing is faster (in a nanosecond).

Architecture of ANN

A neural network consists of three layers. The first layer is the input layer. It contains the input neurons that send information to the hidden layer. The hidden layer performs the computations on input data and transfers the output to the output layer. It includes weight, activation function, cost function.

The connection between neurons is known as weight, which is the numerical values. The weight between neurons determines the learning ability of the neural network. During the learning of artificial neural networks, weight between the neuron changes.

Working of ANN

Firstly, the information is feed into the input layer. Which then transfers it to the hidden layers, and interconnection between these two layers assign weights to each input randomly at the initial point. Then bias is add to each input neuron and after this, the weight sum which is a combination of weights and bias is pass through the activation function. Activation Function has the responsibility of which node to fire for feature extraction and finally output is calculate. Therefore this whole process is known as Forward Propagation. After getting the output model to compare it with the original output and the error is known and finally, weights are updates in backward propagation to reduce the error and this process continues for a certain number of epochs (iteration). Finally, model weights get updates and prediction is done.

Some Merits of ANN

  • It has a parallel processing ability. It has the numerical strength that performs more than one task at the same time.
  • After training, ANN can infer unseen relationships from unseen data, and hence it is generalise.
  • Unlike many machine learning models, ANN does not have restrictions on datasets like data should be Gaussian distribute or nay other distribution.

Applications of ANN

There are many applications of ANN. Some of them are :

Medical

We can use it in detecting cancer cells and analysing the MRI images to give detailed results.

Forecast

We can use it in every field of business decisions like in finance and the stock market, in economic and monetary policy.

Image Processing

We can use satellite imagery processing for agricultural and defense use.

Conclusion

Artificial neural networks are powerful models to solve the problems. Hope you understood the architecture, working, applications and advantages of the artificial neural network. If you want to get deep dive into ANN you can refer the attached link.

Reference

https://en.wikipedia.org/wiki/Artificial_neural_network

Thankyou and Keep Learning!!!

Written by 

Shubham Gupta is software trainee at knoldus. He is practising Devops - Docker, Jenkins, Ansible, Kubernetes. He is passionate about DevOps technology and cloud computing. He like to watch movies and playing online games.

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