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kwater-ailab

laminage

laminage

largescalenerfpytorch

1. Non-official implementation of Block-NeRF and Mega-NeRF in Pytorch. 2. Train your large-scale NeRF in the wild. 3. Weekly classified NeRF literature.

leafmap

A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment

learning-resources

Collection of resources recommended by community members for learning various python programming topics.

lstm-data-assimilation

ludwig

Data-centric declarative deep learning framework

master

A machine learning course using Python, Jupyter Notebooks, and OpenML

metsim

Meteorology Simulator

metsim-tutorial

A tutorial for MetSim

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

mizuroute

NCAR/RAL streamflow routing model

ml4floods

An ecosystem of data, models and code pipelines to tackle flooding with ML

mlhub-tutorials

Tutorials to access Radiant MLHub Training Datasets

model_preprocessing

Model_Preprocessing

msda_crashcourse

Crash Course session on multiscale data assimilation

mts-lstm

Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network

multimodal-flood-tweet-classification

While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not based on its text remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. In this study, we designed a multilingual multimodal neural network that can effectively use both textual and hydrological information. The classification data was obtained from the Twitter-streaming API using flood-related keywords in English, French, Spanish and Indonesian. Subsequently, hydrological information was extracted from a global precipitation dataset based on the tweet’s timestamp and locations mentioned in its text. We performed three experiments analyzing precision, recall and F1-scores while comparing a network that uses hydrological information against a network that does not. Results showed that F1-scores improved significantly across all experiments. Most notably, when optimizing for precision the network with hydrological information could achieve a precision of 0.91 while the network without hydrological information failed to effectively optimize. Moreover, this study shows that including hydrological information can assist in the translation of the classification algorithm to unseen languages. Tweets filtered using this network can be used to more effectively organize disaster response, validate and calibrate flood risk models, and task satellites among other applications.

nasa_gpm

Scripts for get precipitation from arthurhou.pps.eosdis.nasa.gov

nerf

Code release for NeRF (Neural Radiance Fields)

nerf-factory

An awesome PyTorch NeRF library

nerf-pytorch

A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

nerfstudio

A collaboration friendly studio for NeRFs

nersc_ml_course

internal ML course/practical demonstration intern to NERSC

network_analysis

networkx

Network Analysis in Python

networkx_examples

neural_prophet

NeuralProphet: A simple forecasting package

neuralhydrology

Python library to train neural networks with a strong focus on hydrological applications.