Deep Learning Architecture — Layers That Learn

DEEP LEARNING is a kind of artificial intelligence built from NEURAL NETWORKS — software inspired by how brain neurons connect. The "deep" part refers to having many LAYERS of neurons. Each layer takes the output of the previous one and transforms it. Early layers learn simple features (edges, curves). Middle layers combine those into shapes. Late layers recognize whole objects (cats, faces, words).

Common architectures. CNN (Convolutional Neural Network): great at images — looks for patterns in small patches. RNN (Recurrent Neural Network): processes sequences (text, time series) by remembering previous inputs. TRANSFORMER: today's state-of-the-art for language and vision — uses "attention" to figure out which parts of input matter most. ChatGPT and Claude are transformers.

Training a deep network requires huge amounts of data and computing power. Modern transformers like GPT-4 have hundreds of billions of parameters and were trained on vast text corpora. Despite this, the IDEAS — neurons, layers, training by gradient descent — date back to the 1950s and 1980s. Today's breakthroughs are scale + clever architectures + better training tricks.

Deep learning powers most of the AI you use daily — image search, voice assistants, translation, fraud detection. Knowing the architecture lets you understand what these systems can and can't do.