CristinaMarsh

ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

ElegantRL

Massively Parallel Deep Reinforcement Learning. 🔥

timesfm

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

awesome-TS-anomaly-detection

List of tools & datasets for anomaly detection on time-series data.

awesome-kan

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold Network field.

zarr-python

An implementation of chunked, compressed, N-dimensional arrays for Python.

all-of-statistics

Self-study on Larry Wasserman's "All of Statistics"

whitebox-tools

An advanced geospatial data analysis platform

geojson

Python bindings and utilities for GeoJSON

xarray-spatial

Raster-based Spatial Analytics for Python

daam

Diffusion attentive attribution maps for interpreting Stable Diffusion.

xplique

👋 Xplique is a Neural Networks Explainability Toolbox

pyextremes

Extreme Value Analysis (EVA) in Python

Satellite_Imagery_Analysis

Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

xMIP

Analysis ready CMIP6 data in python the easy way with pangeo tools.

goes2go

Download and process GOES-16 and GOES-17 data from NOAA's archive on AWS using Python.

spyndex

Awesome Spectral Indices in Python.

EMIT-Data-Resources

This repository provides guides, short how-tos, and tutorials to help users access and work with data from the Earth Surface Mineral Dust Source Investigation (EMIT) mission.

skai

SKAI is a machine learning based tool for performing automatic building damage assessments on aerial imagery of disaster sites.

NSIDC-Data-Tutorials

Jupyter notebook-based tutorials to learn how to access and work with select NSIDC DAAC data.

xlstm-cuda

Cuda implementation of Extended Long Short Term Memory (xLSTM) with C++ and PyTorch ports

geo-notebooks

deepdespeckling

PyESD

Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.

the-coding-club

Explore the pathway toward scaled-up scientific analysis with Earthdata in the cloud. These activities focus on the community with limited knowledge of the AWS cloud, which is still the majority.

EAEE4000_ML_for_Env_Eng_Sci

course_ml_ats

machine learning for the atmospheric sciences - a CSU course

icesat2-book

Interactive Jupyter Book for wrangling, visualizing, and analyzing ICESat-2 sea ice thickness data

GRACE-FO-OpenToolkit

Code and Scripts for the GRACE mission