Deep learning's main advantage is that it can handle massive amounts of data — particularly unstructured — well. Getting started doesn't have to be hard by leveraging publicly available pre-trained deep learning models to begin.
Talking about AI is increasingly complex because it’s often used alongside (or even interchangeably with) the terms machine learning (ML) and deep learning (DL). Why do people use these terms relatively interchangeably, and what are the distinctions?
A deep learning algorithm is able to learn hidden patterns from the data by itself, combine them together, and build much more efficient decision rules. That’s why it can deal with problems that a human brain could not understand — the value of deep learning is this automatic pattern identification capability. This means handling more complex problems, such as understanding concepts in images, videos, texts, sounds, time series, etc.
But don’t think of deep learning as a model learning by itself. It still requires properly labeled data, an evaluation of the model results, and of course an evaluation of the business value it will bring. And the lack of precisely labeled data is one of the main reasons deep learning can have disappointing results in some business cases.