Let’s start by explaining what is Neural Network and why is it so popular…
What is Neural Network
Neural Network or Artificial Neural Network(ANN) is something very similar to a brain. Just like neurons in the brain are responsible for all of our task similar to that in ANN also consist of neurons, A “neuron” in a neural network is a simple mathematical function capturing and organizing information according to an architecture of the provided Dataset.
Neural Network is a brain of your computer, the more data you will feed into it, more quickly it will be able to learn. You can take data to be fed as a series of Algorithm and it will provide you with the best possible result. Neural networks can adapt to changing input so the network produces the possible outcome without the need to redesign the output criteria.
History of Neural Network
The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits. But the technology available at that time did not allow them to do too much.
Let me now elaborate more on Neural Network and how it work
Let me start with an example, suppose you are walking down the street and encounter a street lamp, its the first time you are seeing anything like that so you don’t know what exactly it is, you walk right into it and get hit. The next time you see a lamp post you try to maintain a distance but your shoulder hits the lamp post, the third time you will definitely maintain a good distance. You have learned from the experience you had in a similar way Neural Network learn from the data set provided to them.
Consider a child they ask so many questions to learn things around them so that next time if they will see something new they will try to guess based on the information provided to them earlier.
Once the network has been trained with enough learning examples, it reaches a point where you can present it with an entirely new set of inputs it’s never seen before and see how it responds. For example, suppose you’ve been teaching a network by showing it lots of pictures of chairs and tables, represented in some appropriate way it can understand, and telling it whether each one is a chair or a table. After showing it, let’s say, 25 different chairs and 25 different tables, you feed it a picture of some new design it’s not encountered before — let’s say a chaise longue — and see what happens. Depending on how you’ve trained it, it’ll attempt to categorize the new example as either a chair or a table, generalizing on the basis of its past experience — just like a human.
There are chances of errors also to calculate those chances so that it will not occur again in the future a term called Backpropagation is used. In a similar way as we learn from our mistakes and try not to repeat. When it is wrong, an error is calculated and the values at each neuron and synapse are propagated backward through the ANN for the next time.
What is Backpropagation
Backpropagation is an algorithm or a method used to minimize error using a technique called delta rule or gradient descent.
why we need backpropagation
It’s our guide help us to avoid getting lost in a junk of data.
Where it can be used
Many of the things we all do every day involve recognizing patterns and using them to make decisions, so neural networks can help us out in zillions of different ways. They can help us forecast the stock market or the weather, operate radar scanning systems that automatically identify enemy aircraft or ships, and even help doctors to diagnose complex diseases on the basis of their symptoms.
Why I have a title “something you need to know about Neural Network” is because of the fact that it is a vast syllabus to cover, which can’t be done in one article. Although I have tried to keep it brief which will give you the overview of Neural Network and the terms used inside it.
I am always open to learning so please provide your valuable points which I may have missed out or I should have known about this topics.
If you didn’t understand anything explained here you can comment down and I will reply to them ASAP.