COOLBOX FIELD NOTES
Learn by making.
Choose a hands-on guide for machine learning, visualization, finance, probabilistic models, or data structures. Switch between language examples where CoolBox supports them; unavailable APIs are called out clearly. Looking for setup help? Installation guides live in their own section.
Explore PCA
Fit a two-component projection and use explained variance to interpret it.
Open tutorial 02 / DEEP LEARNINGTrain a tiny neural network
Build a small network to learn XOR with tabbed Python, Go, Rust, and C++ examples.
Open tutorial 03 / VISUALIZATIONGraph a PCA projection
Compute PCA with supported bindings and plot projections with CoolBox’s built-in graphics APIs.
Open tutorial 04 / TREE ENSEMBLESTrain boosted trees
Fit and tune a gradient-boosted classifier in Python, Go, Rust, or C++, then compare decision surfaces.
Open tutorial 05 / FINANCEPrice options and explore risk
Use Black-Scholes, interpret Greeks, and compare volatility scenarios in Python, Go, Rust, and C++.
Open tutorial 06 / PROBABILISTIC MODELSReason with Bayesian networks
Define a discrete network and update probabilities from evidence, with an exact inference walkthrough.
Open tutorial 07 / DATA STRUCTURESScreen membership with EGH filters
Insert IDs, measure false positives, and combine a compact probabilistic filter with exact lookups.
Open tutorialExamples are based on the CoolBox repository and documentation .