A notable subfield of machine learning is deep learning. Deep learning (DL) today largely refers to the use of a specific type of machine learning model—one made up of neural networks with many layers. Deep neural networks (DNNs) can identify very complex patterns in their input data, making deep learning a powerful technique. Today, DNNs represent the state of the art in many applications involving voice, video, and pictures, and are increasingly finding use in natural language processing and tabular data. The unfortunate drawback of deep learning is that training these models can require substantially more labeled data, time, and computational horsepower than traditional ML models.
Over the past several years, enterprises have begun exploring the benefits of applying machine and deep learning to deliver business results in a variety of areas. Industries spanning healthcare, publishing, finance, manufacturing, and more are increasingly turning to ML and DL to improve facets of their business. Potential applications include everything from decision-making, customer service, recommendations, and even building entirely new offerings like self-driving vehicles.