Deep learning may seem like a complex process (because it is) yet you may have used it or experienced the benefits of this type of machine learning technology already. Businesses of all sizes have been using it for applications like facial recognition and image classifications. So how can you tell if deep learning can help your small or enterprise-sized business?
First, let’s explore what deep learning means. Deep learning is a type of machine learning that uses algorithms modeled after human neural networks to process massive amounts of data and make predictions. Deep learning algorithms perform tasks over and over again, making small tweaks each time, to see what kind of results it achieves, all the while learning how to essentially “think” on their own. It’s this thoughtful, learning, “deep” process that allows these algorithms to solve complex problems. The more data available, and the more the algorithms spend time learning, the better they become.
Deep learning technology has become more widely available, as illustrated in the following examples.
Human-powered support, whether in person, on the phone or online is expensive. Depending on the applications, chatbots can reduce costly customer support expenses. When they leverage the power of deep learning’s neural network, chatbots provide intelligent responses to user questions for both internal and external facing support. Used with voluminous knowledge bases or complicated items for sale, chatbots offer round-the-clock service that doesn’t need a human to answer complicated questions. Read more about how to build a chatbot.
With deep learning, facial recognition is becoming more and more effective, especially as it learns to recognize people after a new haircut, difference in facial hair, or even with and without masks. It has a strong benefit to security applications, and is even being discussed as a way to prevent financial fraud by using a facial scan to pay for goods and services. PopID is an example of a company that recently rolled out facial recognition payments in a handful of locations in California.
Deep learning is saving medical researchers huge amounts of time by predicting how certain drugs could interact in patients with cancers and other types of diseases. Finding effective drug therapies is a long and arduous process, but with deep learning huge amounts of data can be processed to synthesize drugs that would have a higher probability for success. It weeds out incompatible drug combinations that can save decades worth of research.
Healthcare is an industry weighted by data, manual reporting and gobs of paperwork. It presents a wonderful opportunity for deep learning’s contributions to consider patient data to better predict illness, or suggest treatment. Medical professionals could use these predictions to spend more of their days fulfilling their healing roles rather than doing paperwork. One example is blood sugar monitoring app that considers a multitude of contexts to alert those with diabetes to take actions in their day-to-day lives that can prevent diabetic complications, that, left forgone for too long or occurring too often, can lead to serious problems down the road.
Marketing is one industry where machine and deep learning have saved countless human hours sifting through clusters of data to predict trends in consumer behavior and preference. Algorithms take into account age, gender, past purchases, time of day, holidays, and other various contexts. Consider what factors may affect when your consumers decide to buy, or decide to hold off spending? How might nuanced contextual situations also come into play? Deep learning predicts when a consumer may be ready to convert, or how to best make contact when they are in a position to make a purchase decision.
Deep learning models are designed to predict patterns, and can be used in a wide array of applications. If your business has access to large amounts of data and would benefit from predictive technology, it may be time to consider if you can benefit from deep learning technology. First, you may want to consider if your data is ready for machine learning and deep learning. Then, consider talking to a data scientist to see how this fascinating technology could be leveraged for your next project.
Reach out to a Stratorsoft data expert to get your questions answered.