Deep learning is a branch of machine learning built on neural networks with many layers, hence "deep". The extra layers let the system learn increasingly abstract features, for example from edges to shapes to a recognisable product.
Where traditional machine learning often needs people to decide which features matter, deep learning figures them out from raw data such as images, audio and text. The trade-off is that it typically needs much more data and computing power to train well.
It powers many technologies that operators now use: speech recognition, image recognition for shelf and stock monitoring, video analytics, language translation, and the large language models behind modern chat assistants. Simpler tasks such as forecasting a stable product's sales may still be handled well by lighter methods.
Not every problem needs deep learning. For small datasets or tabular business data, simpler models can be cheaper, easier to explain and just as accurate. Beware vendors who add "deep learning" to descriptions without showing results. Ask what it was trained on, how it was tested and how it performs in your environment.
Deep learning is the engine behind the current wave of AI, and knowing the term helps you separate genuine capability from marketing language.