There are many applications which we are using in our day to day life which involve the application of the neural network, the neural network has become a great part of our life and revolutionizing the technology.
With the help of the neural networks, we are trying to implement the way our brain functions and how neurons interact with each other and the brain capacity to learn from its experience and bring back information from different regions of the brain and bring separate information that is stored in different parts of the brain. We are trying to incorporate all these functions in developing artificial intelligence and this can only happen with the help of neural networks. This is a technology where the learning model is structured in the form of neurons and each neuron is a data point where the computer can store information and modify as the new data keeps coming in, these neurons are upgraded based on the information that it is receiving and the interconnection of such neurons helps the whole artificial neural network to train as a human brain.
So what are the differences between a normal computer and a neural network, any normal computer although we can achieve a lot of computational power such as those in the supercomputers, which apply in the concept of parallel processing to achieve the higher processing speed it can compute many problems at the same time although they may have great computational power they cannot train the data or learn with the help of the neural network. The supercomputer may be able to compute a normal calculation that took a traditional computer one minute, it may be able to complete it in only one second. When we talk in cases of face recognition or speech detection they may fail now.
For example, the human brain is not able to calculate a good length of mathematics in a given period but computers are gate in doing it, but a human can detect the faces that he or she may have seen on his first-day office and if the face has been altered it can detect it again for example if some colleague has altered his beard style the human brain will detect it on the next day also but for the same problem the supercomputer will not able to detect unless and until it has the image where the person has an image with the same beard style.
So to detect all kinds of emotions and changes on the face of the person, a supercomputer must be provided with all of the faces to be detected but if there is a new kind of face by the same person and the data has not been provided to the supercomputer it will fail to recognize it. Ultimately cannot detect the face of the person but here the neural network is based on the learning algorithms which are continuously training the data that it has in there stored database and from there it has the data to recognize a new data input with the help of its learning algorithm, Unlike the supercomputer, it will not be able to detect in the new data face but the neural network will.
Google Assistant is a great example of The Uses of the neural network in our day to day Technology, if you use Google Assistant about ‘what is the weather today’ then it will start to recognize the keyword that exists in the questions and when it has been recognized the keywords for example ‘weather’ and ‘today’ it will start to map already existing data sets and use natural language processing and then to recognize the closest Google search that is suited based on your history and your nearby location, all this in combination work to give you the best possible result and it is all possible with the help of the neural networks. It can perform and understand your query also it can understand various versions of the same phrase as a human can process the languages.
The work of the neurons in the brain is to take the input from the senses for example if you touch a hot surface then the Skin receptor will provide the appropriate input to the brain for removing her hands from away from the surface, in the same way then neural network takes the input from some external sources it can be a new input which it did not have before and then based on this new input it will compare to its old data sets if it can match some part of it and after this matching process It can interpret what the information that it has received as new input is all about. When it has integrated the information it can generate the response that it has to provide as the output to the user.
Let’s talk about some of the functioning of how the artificial neural network work in overview when the inputs are provided such as some new keywords that it has received when you spoke in the Google Assistant then it divides the keyword and add some weight to the particular keywords this adding of the weight to the input is called a summation input function also the neural node is divided into one summation function. Another part into the activation function the work of the summation function is to add up all the weighted inputs that it has received from the source and then after adding up of all the weighted input it is passed into the activation function which can generate the appropriate response at that particular moment in passing it to the next neuron does the same thing do it and in this way, the whole function of the artificial neural network is done.