Before any line of model code, the *why* of Keras. A framework that put 'designed for human beings, not machines' on its first page in 2015 and is still, in 2026, the high-level API a huge slice of published deep-learning work reaches for — that's not an accident. This track tells you who built it, why it survived three engine wars, and when picking it is the right move (vs PyTorch / JAX raw).
The 2015 bet
François Chollet created Keras in March 2015 while working at Google. His thesis was radical for the time: deep learning should be accessible to everyone, not just PhD researchers with years of framework experience.
Before Keras, building a neural network meant pages of boilerplate — declaring a computation graph by hand, managing sessions, and wrestling with low-level tensor ops just to train a two-layer net. Chollet saw that friction as an unnecessary barrier and set out to build an API "designed for human beings, not machines."
The principle underneath everything
His guiding line: "Being able to go from idea to result with the least possible delay is key to doing good research." The claim hiding in that sentence — that developer experience is a research accelerator, not a cosmetic luxury — became the foundation of every Keras design decision that followed. Ten years on, it's still the lens that explains why the API looks the way it does.