Deep learning is a branch of machine learning that uses artificial neural networks with many layers to learn directly from raw data: images, sounds, texts, signals. Each layer extracts increasingly abstract features, from the edges in an image up to the whole object.
These networks are very loosely inspired by the brain, but remain mathematical functions whose parameters are adjusted during training, using large amounts of examples and substantial computing power, usually GPUs.
The boom dates back to the early 2010s, when these methods clearly outperformed classic approaches in image recognition. Deep learning underpins computer vision, speech recognition, machine translation and, with the Transformer architecture, large language models. The reference frameworks are PyTorch and, to a lesser extent, TensorFlow and JAX.
Why it matters when hiring
Deep learning covers research (new architectures), large-scale training and integrating existing models: three different profiles. On a resume, look for models trained on real data, with the associated constraints (data volume, GPUs, deployment), or publications for a research role. In an interview, ask how the candidate diagnosed a model that was not learning.