Domain Neighborhood
Calculus
Rates of change and accumulation. Calculus is the language behind gradients, optimization, continuous-time dynamics, and why backprop works as efficiently as it does.
The first route object appears when this domain has a published notebook.
f(x)dxdf/dxlocal changeLearner lensWhat makes this domain feel navigable?
Take this lensStart with the first published notebook when this domain opens.
Recommended Route
Start here, then follow the prerequisites forward.
This sequence is ordered for learning rather than inventory. Published notebooks with an unavailable prerequisite—or a same-domain route step that depends on one—are labeled in the full inventory instead of being presented as ready steps.
No complete recommended route is public yet. Chain Rule and Functions are still below the publish bar. The notebooks below remain readable as standalone material, but they are not presented as a complete prerequisite sequence.
All Published Notebooks
Browse the territory.
Computation Graphs
A computation graph breaks a calculation into nodes so values flow forward and sensitivities flow backward.
Derivatives
The derivative is an instantaneous rate of change: the slope you get when a secant line becomes a tangent line.
Reverse-Mode Automatic Differentiation
Reverse-mode autodiff computes gradients by sending cotangents backward through a computation graph.
Backpropagation
Backpropagation applies reverse-mode autodiff to neural networks so one scalar loss can train many parameters.
4 published notebooks are readable now but held out of the recommended route until the named prerequisite notebooks are published.