Deep Learning
A subset of machine learning that uses neural networks with many layers to learn complex patterns from large amounts of data.
What Makes Learning "Deep"
Deep learning gets its name from the depth of neural networks - specifically, the number of hidden layers between input and output. A shallow network might have one or two hidden layers. A deep network can have dozens, hundreds, or even thousands. Each layer learns to recognize increasingly abstract features.
Consider image recognition. The first layers might detect edges and basic shapes. Middle layers combine those into parts like eyes, wheels, or leaves. Deeper layers recognize whole objects - faces, cars, trees. This hierarchical learning is what allows deep learning to tackle problems that stumped AI researchers for decades.
The Deep Learning Revolution
Before deep learning took off around 2012, most AI systems relied on hand-crafted features. Engineers had to manually define what the system should look for. Deep learning changed everything by letting the system discover relevant features on its own.
The catch? Deep learning is hungry. It needs massive datasets and serious computing power to train effectively. That's why it didn't become practical until we had big data, powerful GPUs, and better training techniques. But when those pieces came together, the results were stunning - systems that could match or beat humans at tasks like image classification, speech recognition, and game playing.