Deep Learning

What Deep Learning is

Deep learning stacks simple mathematical units into deep networks. Each layer transforms the previous one, so early layers capture low-level structure — edges in an image, character patterns in text — while later layers compose those into higher-level meaning.

How it works

Networks are trained with backpropagation: a forward pass produces a prediction, a loss is computed, and gradients flow backwards so every parameter is nudged in the direction that reduces error. This is repeated over huge datasets on GPUs or specialised accelerators for hours to months.

Why it matters

Deep learning is the reason image recognition, speech and language all improved at once after 2012. Its central lesson — that scale in data, parameters and compute reliably buys capability — shaped the entire era of foundation models.

Common uses

• →Speech recognition and text-to-speech
• →Object detection in autonomous systems
• →Protein structure and molecule property prediction
• →Every current large language model