Deep Learning

Remember neural networks? Layers of artificial neurons that process data? "Deep learning" is just what happens when you stack a LOT of those layers together. That's it. The "deep" refers to the depth of the network — more layers = deeper = deep learning.

Think of it like a game of telephone, except it actually works. Data enters the first layer, gets processed, passes to the next layer, gets processed more, and so on through dozens or hundreds of layers. Each layer extracts more complex patterns. Early layers might recognize edges in an image. Middle layers recognize shapes. Deep layers recognize faces.

The reason deep learning took over AI is because it scales. Give a deep network more data and more computing power, and it keeps getting better. That's the recipe behind every AI breakthrough in the last decade — more layers, more data, more GPUs.

Because "deep learning" is the buzzword people use when they want to sound impressive about AI. Now you know it just means "a neural network with lots of layers." You can nod knowingly at dinner parties. You're welcome.

Deep learning uses multi-layer neural networks (typically 10-1000+ layers) to learn hierarchical feature representations. It became practical with GPU computing, large datasets, and architectural innovations (ResNets, batch normalization, dropout). Deep learning powers computer vision (CNNs), natural language processing (Transformers), speech recognition, and generative AI.