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Starred repositories
Build, deploy, and orchestrate AI agents. Sim is the central intelligence layer for your AI workforce.
westamine / Hands-On-Reinforcement-Learning-with-Python
Forked from PacktPublishing/Hands-On-Reinforcement-Learning-with-PythonHands-On Reinforcement Learning with Python, published by Packt
Hands-On Reinforcement Learning with Python, published by Packt
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
A modern, modular, and fully customizable LaTeX template designed to replace traditional .doc and .docx editors with a powerful, reproducible, and automated document workflow.
Some Conferences' accepted paper lists (including AI, ML, Robotic)
Some Conferences' accepted paper lists (including AI, ML, Robotic)
Evidential Deep Learning in PyTorch
Implementation of "Evidential Deep Learning to Quantify Classification Uncertainty" proposing a method to quantify uncertainty in a neural network.
Compendium of free ML reading resources
Integration examples and showcase of <geosys/>platform capabilities.
Code examples for the book titled Introduction to GIS Programming
A python library for creating ISCE3-based OPERA RTCs for all modern SAR missions (eventually)
List of Computer Science courses with video lectures.
12 Lessons to Get Started Building AI Agents
Este repositório apresenta uma adaptação demonstrativa do módulo S2DR3, criado pela equipe Gamma Earth.
A complete computer science study plan to become a software engineer.
This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as described in "A General Framework for Uncertainty Estimation in…
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
A comprehensive benchmark for real-world Sentinel-2 imagery super-resolution
Documentation that simply works
Datasets for deep learning with satellite & aerial imagery
Extra documentation about using ODC with Jupyter Notebooks
A. Moghimi, A. Mohammadzadeh, T. Celik and M. Amani, "A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images," in IEEE Tr…
Collection of Tools and Papers related to Adapters / Parameter-Efficient Transfer Learning/ Fine-Tuning
The official implementation for ECCV22 paper: "FOSTER: Feature Boosting and Compression for Class-Incremental Learning" in PyTorch.