ayat-khairy

ayat-khairy's Stars

• tqdm/tqdm

  :zap: A Fast, Extensible Progress Bar for Python and CLI

  Language:Python28.3k2069901.3k

• AtsushiSakai/PythonRobotics

  Python sample codes for robotics algorithms.

  Language:Python22.7k5103526.5k

• ParthJadhav/Tkinter-Designer

  An easy and fast way to create a Python GUI 🐍

  Language:Python9k88278823

• pytorch/botorch

  Bayesian optimization in PyTorch

  Language:Jupyter Notebook3.1k51515390

• facebook/Ax

  Adaptive Experimentation Platform

  Language:Python2.3k69733303

• NVlabs/curobo

  CUDA Accelerated Robot Library

  Language:Python73217178109

• dilinwang820/Stein-Variational-Gradient-Descent

  code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

  Language:Python379122120

• NVIDIA/modulus-makani

  Massively parallel training of machine-learning based weather and climate models

  Language:Python22212333

• ahq1993/MPNet

  Motion Planning Networks

  Language:C216102250

• uber-research/TuRBO

  Language:Python1817452

• alan-turing-institute/deepsensor

  A Python package for tackling diverse environmental prediction tasks with NPs.

  Language:Python7267015

• abeleinin/gp-navigation

  Gaussian Process-based Traversability Analysis for Terrain Mapless Navigation

  Language:Python48112

• bsciolla/gaussian-random-fields

  Generator of 2D gaussian random fields

  Language:Jupyter Notebook48007

• microsoft/vi-hds

  Variational inference for hierarchical dynamical systems

  Language:Python468318

• KaleabTessera/PRM-Path-Planning

  Implementation of Probabilistic Roadmap Path Planning Algorithm.

  Language:Python36306

• Ulvetanna/HypoSVI

  Hypocentral earthquake location using Stein-variational gradient descent and deep neural network travel-time formulations

  Language:Python289

• SheffieldML/hgplvm

  Hierarchical Gaussian process latent variable model.

  Language:MATLAB17306

• dixantmittal/fast-rrt-star

  Vanilla implementation of Rapidly Exploring Random Tree (RRT), Rapidly Exploring Random Graph (RRG) and Rapidly Exploring Random Tree* (RRT*)

  Language:Python15205

• riley-knox/RRT

  Explore a space and find a path around obstacles using a rapidly-exploring random tree (RRT)

  Language:Python151

• climatechange-ai-tutorials/lulc-classification

  Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.

  Language:Jupyter Notebook137

• MiaoDragon/Hybrid-MPNet

  Language:Python9304

• sashalambert/stein_lib

  Language:Python9210

• lesurJ/RRT

  Rapidly-exploring Random Tree

  Language:Python7200

• danielstrizhevsky/motion-planning

  Implementations with interactive visualizations of multiple motion planning algorithms.

  Language:Python6111

• climatechange-ai-tutorials/bioacoustic-monitoring

  This tutorial presents an "agile modeling" approach that enables users to build custom classifier systems efficiently for species of interest using transfer learning, audio search, and human-in-the-loop active learning.

  Language:Jupyter Notebook5003

• NicolasDurrande/guepard

  Language:Python51

• kenzaxtazi/icml23-gpframe

  Code and data for 'Beyond Intuition, a Framework for Applying GPs to Real-World Data' presented at the Structured Probabilistic Inference and Generative Modeling Workshop at ICML 2023.

  Language:Jupyter Notebook40

• boschresearch/Hierarchical-Hyperplane-Kernels

  Official implementation of "Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems" (AISTATS 2023)

  Language:Python3303

• hichoe95/RRT-Rapidly-Exploring-Random-Trees-

  Language:Jupyter Notebook210

• climatechange-ai-tutorials/flood-monitoring

  Floods in coastal areas can be extremely destructive natural hazards resulting in societal and economical damage. In this tutorial, explore how to predict building and population density to understand the potential impact from a flooding event.

  Language:Jupyter Notebook12