Deep Learning
Deep learning is a kind of machine learning that uses neural networks with many layers stacked on top of one another. The word "deep" refers to that depth, not to any special cleverness. Each layer learns to spot slightly more complex patterns than the one below it, so the network can build up from simple features to rich understanding. It is the approach behind most of the AI breakthroughs of the past decade, from image recognition to chatbots.
Imagine sorting a huge pile of photographs. The first layer of workers only notices the simplest things: edges, blobs of colour, and light or dark patches. They pass their findings up to a second layer, which combines those edges into shapes like curves and corners. A third layer assembles those shapes into recognisable parts such as eyes, wheels, or leaves. By the time you reach the top, the network can confidently say “this is a cat”. Each layer builds on the one beneath it, which is the heart of deep learning.
Deep learning is a branch of machine learning, the broader idea of computers learning from examples rather than following hand written rules. What makes deep learning special is that stacking many layers lets a system learn extremely subtle patterns without a human needing to describe them in advance. Older methods often needed experts to tell the computer what features to look for; deep learning works those out for itself.
This matters because it unlocked tasks that had stumped computers for decades. Reliable speech recognition, translation that actually reads well, and the large language models behind modern chatbots all rest on deep learning. The trade off is that these systems are hungry: they need vast amounts of data and considerable computing power to train, which is why the field is dominated by well resourced labs and companies.