•1 min read•from Machine Learning
Parax v0.5: Parametric Modeling in JAX [P]
Hi everyone!
Just sharing an update on my project Parax, which caters for "parametric modeling" in JAX.
Previously, Parax was more focused on scientific applications, however I've since generalized it to be a tool useful for any type of JAX work. It now has a strong focus on a clean, extandable API, as well as ensuring the library is entirely opt-in, as opposed to its previous versions which took a more framework-like approach.
Some of Parax's features:
- Derived/constrained parameters with metadata
- Computed PyTrees and callable parameterizations
- Abstract interfaces for fixed, bounded, and probabilistic PyTrees and parameters
- Filtering and manipulation tools
The documentation is available here along with some basic examples. Perhaps the package is of use to someone out there!
Cheers,
Gary
[link] [comments]
Want to read more?
Check out the full article on the original site
Tagged with
#financial modeling with spreadsheets
#natural language processing for spreadsheets
#generative AI for data analysis
#Excel alternatives for data analysis
#rows.com
#financial modeling
#self-service analytics tools
#machine learning in spreadsheet applications
#business intelligence tools
#collaborative spreadsheet tools
#cloud-based spreadsheet applications
#data visualization tools
#data analysis tools
#spreadsheet API integration
#Parax
#parametric modeling
#JAX
#API
#PyTrees
#scientific applications