Deep Learning
Deep Learning
Many-layered neural networks that learn their own features instead of being handed them.
Grasp
Classical machine learning needs you to design the features. Deep learning learns them, by stacking many simple transformations so that each layer builds a slightly more abstract representation than the last.
That is the entire conceptual leap, and it is why deep learning took over any domain where features are hard to hand-design: images, audio, and language. Give a network enough depth, enough data and enough compute, and the representations it discovers beat the ones experts spent decades designing.
The costs are real and worth stating plainly. Deep models need orders of magnitude more data, they are expensive to train, they are difficult to debug because the failure is distributed across millions of parameters, and on ordinary tabular data gradient-boosted trees usually still win. Reach for depth when the input is unstructured, not by default.