With this new technology taking over, you probably are thinking to yourself that you need to jump on this bandwagon and build something with the help of a neural network. For a tutorial and ‘How to’ on how to utilize this technology continue reading!
Examples of Neural Networking
First, we’re going to be addressing what a neural network even means. This process can be used in simple or more complex situations. Some places that you can find this process in use are;
- Banking
There have been a lot of banks utilizing this method of predictive networking. Their goal is to transition from old technologies and bring in new ones. Predictive networking has been useful in numerous banking operations from predicting the amount of money needed in the facility to detect fraudulent credit card transactions.
- Advertising
This is major in the advertising world because they can determine their own relevance to their customers. It is also great for retargeting. They are able to get the most research from using this neural network technology.
- Healthcare
This technology is even helpful in the scientific world because it helps solve or enhance a lot of problems that they had before. For example, they have been able to enhance their clinical imaging results with the use of this new technology.
- Car Dealerships
In the attempt to create a self-driving car, they won’t make it live on real roads. But it can be done! This new neural technology is transforming the world as we know it, slowly but surely.
What Exactly is Neural Networking?
This new technology is based on the neurons inside a human’s brain. Hence the name ‘Neural Networking’. It is all one big system that talks to each other. The neurons and synapses in our brain communicate and tell our body what to do. That is the same process in which this networking technology works. Not to mention our brain has over 100,000 neurons working in it, so this technology is going to try and mimic that.
More About the Technology
Geoffery Hinton is the maker of it all. This algorithm comes to life with him behind the wheel. And the purpose of it is to attempt to make a map of your desired input and output. Basically what you have and what you want to achieve.
For example, if you are observing the image of a dog and you put that in the input side of the algorithm, the output will come up with the actual word ‘dog’ because it has connected with the information throughout the world wide web and categorized the image to be a picture of a dog.
Guide to the Algorithm
No matter the neural network project you tackle in the future, these rules will always apply.
- First, start simple with a single-layer perceptron just to evaluate the result. If it is successful, then proceed to execution.
- If the previous step is not good enough, try to get your network wider and/or deeper. Add several neurons in your single-layer perceptron. Or, add one layer to the existing network. Observe and, if it is successful, proceed to deployment. If not, then iterate by adding more neurons or layers.
- If even after adding several layers to your network the results still aren’t making out to be successful then you might have to change the layout of your architecture. Test your algorithm with more images and see if the execution of those helps the results of what you are trying to test.
Follow those three steps, and you will get better results.
In The End
The process of building a Neural Network is pretty much like this. Follow these three steps and you will do just fine.
With traditional datasets like those in your company database, you can follow these steps from the very beginning and start to complicate the network. But, for images or texts, it is actually better to just start jumping into the most suitable architecture. Although keep in mind that you’ll want to keep the structure as simple as possible when you are starting the first step.