Instant neural graphics primitives: lightning fast NeRF and more

Library for multivariate function approximation with splines (B-spline, P-spline, and more) with interfaces to C++, C, Python and MATLAB

CSE 571 Artificial Intelligence

Fast radial basis function interpolation for large scale data

Adaptively sampled distance fields in Julia

TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.

A collection of B-spline tools in Julia

The tools for proper interactions between ApproxFun.jl and DifferentialEquations.jl for pseudospectiral partial differential equation discretizations in scientific machine learning (SciML)

Basis Function Expansions for Julia

Universal Function Approximation by Neural Nets

Julia Wrapper to the Tasmanian library

Reinforcement learning algorithms

Course work of Reinforcement-Learning-CS6700

Easy21 assignment from David Silver's RL Course at UCL

An adaptive fast function approximator based on tree search

Practical experiments on Machine Learning in Python. Processing of sentences and finding relevant ones, approximation of function with polynomials, function optimization

Suite of 1D, 2D, 3D demo apps of varying complexity with built-in support for sample mesh and exact Jacobians

Local function approximation (LFA) framework, NeurIPS 2022

Programming assignments of Numerical Methods Sessional Course CSE 218 in Level-2, Term-1 of CSE, BUET

Multivariate Normal Hermite-Birkhoff Interpolating Splines in Julia