An artificial neural network is one of the main data modeling tools used in machine learning. The ‘neural’ in its name means that it is based on how the human brain works. The neuron in the brain is said to be fired off when stimulated.

A neural network tries to replicate the work of the neuron. The neuron takes in the information or data input, processes it by looking for patterns or similarities, and then provides the output. The neuron, which is called a node, is the basic unit of a neural network. I was reading this site, which explained how a node takes input data, gives it weight through coefficients, and sends the output data. Read on if you want to know more about neural network:

1. What Does It Do?

The basic thing that an artificial neural network can do is that it processes data and tries to make sense of seemingly meaningless sets of information. For example, you can feed data into a neural network about the prices of shares in the stock market and the price changes of cryptocurrencies.  

The neural network will try to make sense of this vast amount of data by finding instances of when price decreases in specific stocks coincided with price increases in specific cryptocurrencies. The neural network would then try to find out if there’s any relationship or correlation between the price changes in specific stocks and the price changes in specific cryptocurrencies.

Neural networks can work on raw datasets and detect similarities and patterns. They can also work on labeled datasets and sort these into groups, categories, or classification.

2. Specific Tasks Performed by Neural Networks

Artificial neural networks can do numerous tasks, which are very similar to the processes and tasks done by the human brain. Here are some specific tasks that these networks can do:

• Classification

A neural network can make sense of the raw data that you feed into it. It can sort data and information into groups, classifications, sets, or subsets by looking for traits in the input data and grouping together those that have things in common.

Neural networks separate data that have distinct or different traits. They can detect faces or identify people in images. They can also identify objects in pictures, recognize gestures and motions, detect voices and identify speakers, and transcribe voices into written words. They can even be used to make a robot learn how to draw a self-portrait.

• Clustering

Neural networks can also detect similarities in a dataset and group those similar elements into clusters. The advantage of neural networks is that they don’t need datasets to be labeled before they can sort and cluster. You can feed them datasets that are completely raw and unorganized. This is called unsupervised learning.

Clustering is a very powerful capability because most of the data in the real world are unlabeled and unorganized. Humans can organize and classify data but usually only in small datasets, unless they do it with the help of computers. However, neural networks can process vast amounts of data that haven’t been processed yet.

Another distinct advantage of neural networks is that feeding them with a large amount of data helps them get better at what they do. This happens because a neural network keeps on feeding the inputs into its nodes until it’s able to remove or minimize errors. The neural network thinks that an error is present if there’s a significant difference between what it churned out as output and what it should’ve produced.

• Productive Analytics

The smartest thing about neural networks is that they’re supposed to predict what’s going to happen next based on the data fed to them. An artificial neural network is said to be a powerful matrix combination of the following:

1. Power to take in vast amounts of data, sometimes in volumes not even a person with a computer can process
2. Ability to make sense of these data by detecting characteristics, similarities, and patterns
3. Ability to find correlations between input and output
4. Ability to use what it learned to make calculated predictions

3. Neural Networks Can Learn

One of the most powerful characteristics of a neural network is perhaps its ability to learn on its own. When a neural network finds correlations and makes predictions, it would eventually compare whether the prediction is correct as against reality or ‘ground-truth.’ If it made a wrong guess, the neural network will feed the input data again and try to find out what went wrong. It will identify which weights or coefficients led to the error and make adjustments.

Vast Learning Potential

Artificial neural networks are rapidly changing the way people work and the way things are being done. Their power to make sense of a large amount of data has a big potential to make humans learn more about all the data out there. These data are in almost every human activity, and neural networks are the best tools to clarify what other intrinsic or innate principles all these data have in them. If people can figure out what’s in all these data, the implicit applications are practically limitless.