Deep Learning

Deep learning is machine learning with neural networks deep enough to learn their own features. In classical machine learning a human decided what to measure — word counts, pixel histograms, hand-designed signals — and the algorithm weighted those inputs. A deep network is given raw-ish data and many stacked layers, and each layer learns a representation built from the one below it: edges, then shapes, then objects; characters, then words, then meaning. Nobody specifies those intermediate concepts, which is why the approach scaled to problems where feature engineering had stalled, and why it now underpins essentially every capable language, image, speech, and video model. Three things made it work, and all three are practical rather than theoretical. Data: layers with millions or billions of parameters need enormous training sets, which the internet supplied. Compute: the matrix arithmetic involved suits GPUs, so training that would have taken years became weeks. And architecture: convolutions for images, then transformers for sequences, gave networks the right structural bias for their domain. Remove any one and the field stays where it was in 2010. The costs are the mirror image of the benefits. Because the model learns its own representation, you cannot read its reasoning off the weights — deep models are the reason interpretability is a research field rather than a code review. Because they need scale, training a frontier model from scratch is out of reach for almost every company, which is why the practical workflow is to take a pretrained model and adapt it: fine-tune it, or more often just prompt it. And because they interpolate over their training distribution rather than reasoning from rules, they fail in ways rule-based systems don't — confidently, fluently, and on inputs that look unremarkable. For a SaaS builder the takeaway is that deep learning is rarely something you do; it is something you consume through an API, and your engineering effort goes into the data you feed it and the guardrails around what it returns.