Data-driven techniques like numerical optimization, stochastic gradient descent, and differentiable programming are taking over computer science.
Real-time graphics were relatively slow to adopt, partially because of tools. HLSL is not PyTorch, and you don't want to rewrite all your BRDFs in Python.

Fortunately, you don't have to! With Slang, you can differentiate your existing shader code. And it's getting a lot of attention, including recent Khronos adoption: https://www.khronos.org/news/press/khronos-group-launches-slang-initiative-hosting-open-source-compiler-contributed-by-nvidia
The ability to compute a gradient of a shader function is not enough to make differentiable programming easy. SGD works great in over-parametrized settings, such as neural networks, but it requires a lot of know-how on avoiding local minima, exploding gradients, and dealing with non-convex problems.

This motivated our article, where we explain SGD and optimization from grounds-up, show some possible pitfalls, how to deal with them in practice, and how Slang can help make your existing shader code work in data-driven pipelines.
To make it relatable and practical, we show three non-neural-network applications.

One is automatic computing Jacobians of variable transformations common in Monte Carlo integrals.
One is a surprisingly superfast BC compressor that uses SGD.
Finally, the last application replaces analytical material texture mipmap generation (such as LEAN or Toksvig) with a data-driven approach.