Adaptive control for skid-steer robots using GP-enhanced MPPI for robust navigation and obstacle avoidance on diverse terrains.

A simplistic and efficient pure-python neural network library from Phys Whiz.

A Python library for efficient feature ranking and selection on sparse data sets.

A customized sparse solver wrapper with Numba compatibility

This is a tutorial about Numba-CUDA

Building upon original repo, trying to implement encoder-decoder transformer using CUDA

A small sample of a volumetric Lagrangian Coherent Structure code for GPUs and CPUs leveraging Numba.

Materials for ARC's cluster club session on accelerating scientific python codes

A simple problem to compare execution speed between Numba and C++

Python project which is used single thread, multi thread with Numba jit compiler.

This repository contains an advanced tutorial on optimizing Python code for machine learning applications, focusing on processing large amounts of data efficiently. It covers three powerful libraries: Numba, NumPy, and Polars.

Plasma Particle Dynamics (PPDyn), a python code to simulate plasma particles using Molecular Dynamics Algorithm. Numba JIT compiler for Python has been implemented for faster performance. Detailed documentation can be found at https://ppdyn.readthedocs.io/.

Simulation of plant growth in 2D. Accelerated with Numba

Modern Portfolio Theory (MPT) and Monte Carlo simulations to optimize and backtest a portfolio of various financial assets

A 3D procedurally generated maze game, based off of 2D ray casts. Built in python using limited libraries.

Parallelized N-body code in Python + Numba to simulate galaxy mergers with ~2×10⁵ particles

A 2D Fractal Generator

QuantHedge-MM implements advanced computational methods for pricing and hedging options in markets with stochastic regime shifts. Built for quants and researchers, it extends Black-Scholes to Markov-modulated models.

Single neuronal models encoded in python language numba speed up. Note model waveforms are returned in Neo Format and model currents and voltages are represented with quantities units.

This project implements a CNN from scratch using NumPy and Numba to recognize hand signs representing digits (0-9) from the Sing Language MNIST dataset.