What Is a Neural Network?

Neural networks are primarily used to classify and cluster raw, unlabeled, real-world data. They work behind the scenes of familiar technology such as online image comparison or financial decision-making tools for large corporations. A neural network can also look for patterns in web browsing histories to develop recommendations for users.

Neural networks typically excel at classification tasks, which require labeled datasets for supervised learning. 

For example, neural networks can find visual patterns in thousands of photos and consistently apply labels at a fast rate. Through training, they become good at solving complex, confusing problems. The data scientist doesn’t have to program the neural network with characteristics to distinguish between dogs and cats; the neural network learns to distinguish the most important features itself. 

A neural network can learn to classify any data with a label that correlates to information the network can analyze. 

While they excel at identifying differences, neural networks also work well for clustering or detecting similarities. A learning neural network can analyze millions of data points and cluster them according to similarities. This can be applied to images, emails, voice messages or news articles.

This capability is likewise useful for identifying anomalies, or things that don’t correspond with group characteristics. For example, clustering is used to identify unusual behavior—such as fraud—by identifying data that doesn’t correspond with the most common actions.

Classification and clustering create a static prediction, such as an image correlating to the label of a dog. That identification won’t change over time. Regression analysis gives neural networks the power to predict future states based on past events. A future event becomes just another data point. 

For example, the neural network is able to read a string of numbers and predict the next number most likely to occur. It can apply the same analysis to more complex events, such as predicting when a customer may leave a store or when a piece of manufacturing equipment is likely to fail. 

Regression analysis forms the basis for predictive analytics. By using regression analysis, a data scientist can model the relationship between a dependent variable (the outcome) and one or more independent variables (the input). Regression analysis will reveal any significant relationships between the independent variables and the dependent variable, as well as the strength or weight of that impact. In other words, when the independent variables change, how much and in what way will the dependent variable change?

A basic neural network uses linear regression to manage one input and one output. Multiple linear regression comes into play with many input variables. In this case, each node of the network performs multiple linear regression, weighing each data point as it moves through the layers. The net tests the inputs as it tries to reduce error.

Each node acts as a switch to allow or block the input of the nodes around it through the network. Non-linear regression moves the input through the network until it reaches the final layer of the net.

Neural networks use techniques such as gradient descent and backpropagation to refine their algorithms and find the optimal model for the regression.

Neural networks are integral to the development of machine learning and artificial intelligence applications. At this point, they don’t approach the cognitive abilities of a 4-year-old child. But, they are being used in self-driving cars, facial recognition, language translations and even artistic endeavors such as creating new colors.

The growth of artificial intelligence has been fueled by the lower cost of cloud computing and graphics processing units to manage the flow of images for training. The widespread availability of electronic images and other data already tagged with information makes training easier and faster.

The capabilities to classify, cluster and make predictive decisions have boosted the integration of neural networks in science research, advertising, e-commerce, customer service, preventive maintenance and many other disciplines. Neural networks scan images of the night sky looking for new astronomical details. Messaging filters intelligently separate useful and unwanted emails and voice messages. Linked with sensors, a predictive analytics system can predict when a hydraulic pump on a manufacturing machine will need to be serviced before it fails.

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As neural networks play a growing role across research and commercial applications, the need for data scientists to create and operate them will grow as well. If you’re interested in data science, check out the career and degree program information available through Master’s in Data Science.