jdiaz4302 Goto Github PK

bayesian_stats_course

Notes and code from "Statistical Rethinking: A Bayesian Course with Examples in R and Stan"

chart-challenge-22

#30DayChartChallenge 2022, USGS edition

convtimelstm

Extension of ConvLSTM and Time-LSTM for irregularly spaced images, appropriate for Remote Sensing

cv

deep_devanagari

Using convolutional neural networks to classify handwritten Devanagari (e.g. Hindi) script characters

dem_superresolution

Enhancing the National Elevation Dataset's Digital Elevation Models from 1-arcsecond to 1/3-arcsecond

dockerfiles

Earth Lab repository of docker files suitable for GIS and Deep Learning, allowing consistent work environments within research groups

dog_monitoring_training

Analyzed dog noise while not at home. Also, listen to constant input for loud noise; if loud, reprimands with recordings.

ds-gitflows-jdiaz

Gitflows training repo for Jeremy!

ds-pipelines-targets-1

ds-pipelines-targets-1-course

Intro to Data Analysis Pipelines using `targets` for USGS Data Science

ds-pipelines-targets-2

ds-pipelines-targets-3

dsen2

Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network

fast-influence-functions

flood_style_transfer

Visually predicting aerial flood imagery using neural style transfer

generating-reviews-discovering-sentiment

Code for "Learning to Generate Reviews and Discovering Sentiment"

github-slideshow

A robot powered training repository :robot:

github-upload

jdiaz4302.github.io

kind-pytorch-tutorial

Kind PyTorch Tutorial for beginners

livelossplot

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

markdown-portfolio

mctoolsr

R package to analyze metagenomic (e.g. DNA from soil) data

ml_workshop_series

A series of machine learning lessons focused on teaching practical implementations with scientific data

neuralhydrology

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

pytorch_influence_functions

This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.

pytorchviz

A small package to create visualizations of PyTorch execution graphs

reviewing-a-pull-request

shap_explorations

Getting acquainted with slundberg/shap