This is the Welch Labs video “Why Deep Learning Works Unreasonably Well,” part of the channel’s “How Models Learn” series, created by Stephen Welch and posted in 2025. Welch Labs is known for carefully animated explainers that build mathematical intuition rather than skimming the surface.
The video approaches deep learning through geometry. Welch shows how each layer of a network applies a transformation that folds, stretches, and reshapes the input space, and how stacking these transformations lets a deep network represent boundaries that would be impractical for a shallow, wide network to capture. He connects this picture to the universal approximation idea and to the empirical observation that depth, not just raw width, is what gives modern networks their power.
For a technically curious viewer, this is a satisfying explanation of a question practitioners often take on faith: why these models work as well as they do. It complements the more introductory neural network explainers in the library by going one level deeper into the why.