What are Neural Networks?
Hello Developers! How you doin’? So, today in this article we will try to understand what neural networks are not from funky mathematical equations and code but from a general perspective or intuition. So without further ado, let's get started!
So first comes the WHY? Why we need an artificial neural network? The answer is simple: Human brains interpret the context of real-world situations in a way that computers can’t. So these networks were first developed in the 1950s to address this issue.
So now comes WHAT? What are neural networks? To answer this question, we first need to learn a little about our brain structure from where the inspiration of Artificial Neural Network has come into play.
So basically our human brain is made up of billions of neuron interconnected with each other thus forming a network giving us the power to do things which makes us the most complex living creatures to ever walked upon the earth.
But wait! what is a neuron? So remembering our good old school biology classes… A neuron is a cell that takes a piece of information(input), processes it and transmits the processed piece of information (output) to the next adjacent neuron which similarly does the same work again. As obvious a neuron is the fundamental unit of a neural network.
So when these billions of neurons in our brain (interconnected with each other) do the same job: receiving, processing, and transmitting the information, it makes up our whole biological neural network giving us the capability to learn things, recognize patterns, etc.
So coming back to the point an Artificial Neural Network a.k.a. ANN is a network modeling our biological neural network to do things that our human brain trying to do, basically learning things and recognizing patterns. A neuron in an ANN is known as a node and the interconnections between the neurons are known as a weight.
So when these neural networks are applied to real-world data, these neural networks try to learn and recognize data by continuously improving their learning capability by updating their weight values. Just like in our neural network the connections get stronger when we learn something better, similarly, in an ANN, the weight values get updated until they further can’t be made better. So in this way by continuously learning from their own mistakes (initial weight values) and updating them an ANN derives the power to do classification, recognize unknown patterns, make predictions, cluster similar types of data, etc.
So at last we can say that computers now have the ability to understand the world around them in a very human-like manner thanks to the power of artificial neural networks.
Thank you for giving your valuable time reading this article. If you liked it please appreciate and if felt any improvements do not forget to mention down in the comments. So see ya until next time!
