Machine learning with neural networks stacked many layers deep.
Deep learning is machine learning done with neural networks that have many layers. "Deep" doesn't mean wise; it means lots of layers. Early layers pick up simple things like edges and dots; later layers combine them into eyes, faces and whole objects.
The idea is old, but deep networks were hard to train for a long time. In 2012 a deep network won the ImageNet image contest by a huge margin, and the field switched over. Big datasets, fast graphics chips (GPUs) and deep designs had finally come together. Most of today's translation, speech recognition and large language models are built with deep learning.
Think of it like this
When you learn to draw, you start with lines, then shapes, then faces, then expressions. The layers in deep learning stack up the same way, from easy patterns to hard ones.
For example
Translation apps that turn a whole sentence smoothly into another language run on deep learning.
Records about this
Source-checked moments from AI history.
Established in a paper the combination of GPUs, large-scale data and deep CNNs. The conventional starting point of the current deep learning era
Showed once again that deep networks can be trained, sparking the revival of deep learning in the 2000s