3 Applications of Perceptrons in the Business World
Despite the fact that data scientists, mathematicians, and software engineers alike have built upon the concepts and ideas that were used to create the perceptron many generations ago to develop new deep learning algorithms, such as convolutional neural networks (CNNs), among others, multi-layer perceptrons (MLPs) still have a wide range of applications in the business world as we currently know it today. More specifically, MLPs are ideal for solving complex problems derived from multiple datasets that contain different variables, as these problems will often be too complicated for the human mind to handle.
To illustrate this point further, consider a simple job function in American society today, creating new credit cards. As many credit cards will have as many as 16 digits, and no two people can have the same credit card number, tasking a team of human beings with keeping track of every individual credit card that has been created nationwide would not be feasible. Due to this fact, MLPs are still one of the most commonly used forms of neural networks within the field of data analytics. To this point, some common applications of MLPs in business include data compression, data encryption, and user profiling.
In the midst of our current digital age, virtually all consumers have some understanding of memory and storage constraints that are present within many popular smartphones, tablets, and mobile devices. For instance, many iPhone users will pay a monthly subscription fee to purchase additional cloud storage once the storage on their physical device has been filled up. To this end, one common application of MLPs in the business world is in data compression, as the process of compressing data has numerous benefits, including increased storage capacity and file transfer capabilities, as well as reducing costs related to storage hardware, software, and bandwidth.
Subsequently, MLPs can be used to aid in the process of data compression through the use of pattern recognition. As data compression is predicated on restructuring the data within a particular file type in order to use resources in a more efficient manner, MLPs can be used to identify which forms of data can be removed from the file, without compromising the quality and shape of the said file. In this way, any consumer that has downloaded a file on Apple Music, uploaded a video to a popular streaming website such as Youtube, or saved a picture to their mobile device when browsing a search engine, will have invariably been exposed to the application of an MLP in one way or another.
Another common application of multilayer perceptrons in the business world is in data encryption. The process of encryption is very similar to the process of data compression. However, these two processes do vary in one important aspect, data encryption is designed to conceal the contents of a particular data, making it impossible to read once it had been encoded. For this reason, data encryption is frequently used to protect the personal data of a particular individual, business, or organization, as a cybercriminal that attempts to steal encrypted data will not be able to access the said data without the appropriate encryption key.
In keeping with the similarities between data compression and encryption, just as the multilayer perceptrons can be used to identify the elements within a data file that can be removed without sacrificing the quality of the file, these neural networks can also be used to identify which data elements can be concealed without ruining the quality of an encrypted file. Through the implementation of pattern recognition, an MLP can be used to essentially render a file unusable for everyone but the persona with the correct encryption key. On top of this, some organizations have gone so far as to use MLPs to encode entire databases, in addition to using such neural networks to strengthen the security of a database.
Finally, user profiling is a third way in which multilayer perceptrons have been used to improve the functionality of business organizations. As all business owners will constantly be looking for ways in which they can engage with their customers in meaningful ways, MLPs can help businesses rank their customer base in relation to a multitude of different metrics, such as the amount of money that a given customer has spent at a specific location, the types of products that are the most popular amongst a given demographic, and how receptive customers are to certain forms of promotion and advertising, along with a host of others.
With respect to deep learning algorithms, MLPs can be utilized to classify and cluster information concerning these different types of customer engagement metrics, giving businesses the ability to glean a better understanding of the ways in which they can provide said customers with an improved shopping experience. Using these methods, a business can take advantage of massive amounts of customer data to provide each individual customer with products and services that are catered to their specific desires and needs, while simultaneously saving time and resources that would otherwise have been wasted without such insights.
While data compression, encryption, and user profiling a just three common applications of multilayer perceptrons within the current business landscape, these classical neural networks can be used to extract valuable information from a dataset in a number of other ways as well. Just as the invention of the wheel thousands of years ago created a foundation for the creation of hundreds of other goods and services that are still used today, multilayer perceptrons are the building blocks of many cutting-edge deep learning models that have been developed in the past decade. Likewise, these neural networks will continue to be leveraged by software developers and organizations to create new business solutions in the years to come.