Deep Learning

Deep learning is a branch of machine learning that stacks many layers of artificial neurons so a model can learn its own features from raw data. Each layer transforms the output of the one below it, and training adjusts millions or billions of weights by backpropagating the error from a loss function. The depth is what matters: early layers pick up simple structure such as edges or character patterns, and later layers combine them into higher-level concepts, removing the need for hand-designed features. Three things made it practical: large datasets, GPUs that run the required matrix multiplications in parallel, and architectures suited to each data type. Convolutional networks drove image recognition, recurrent networks handled sequences before transformers replaced them, and transformers now underpin GPT, Claude, Gemini, and Stable Diffusion. Deep learning is a subset of machine learning, not a synonym: on small tabular datasets, gradient-boosted trees still often win. The main costs are data hunger, compute, and opacity. A deep model rarely explains why it produced an output, which is why regulated fields pair it with evaluation and monitoring rather than trusting it directly.