Image augmentation library in Python for machine learning.
-
Updated
Mar 3, 2018 - Python
Image augmentation library in Python for machine learning.
Basically, `paveloom/binder-base` + Julia
Files for running PyStan on Binder
Docker demo for Workflows for Reproducible Research with R & Git
Jupyter book on unsupervised machine learning to detect patterns in illicit trade data
Statistics for Geography
Using R with Jupyter / RStudio on Binder
Try interactiv c++ in your browser, work with c like as Lua or Python. Template for practising with c++ based on xeus-clang in a jupyter lab or jupyter notebook.
Using webdnn inside of binder for exporting models
NLP text recommendation system built in Python using Gensim, spaCy, and Plotly Dash
Some MOOSE examples on binder.
COVID-19 in Germany's Political Discourse
Demo for Stencila & Dar on binder with multiple Stencila documents in one repository
Deep Extreme Cut
Handwriting Synthesis with RNNs ✏️
Demo for Stencila & DAR on binder with Python code
How to create a Binder-ready repository to transform into an interactive computational environment hosted in the cloud and shared so that the analysis code and dataset can be interactively explored.
Add a description, image, and links to the binder-ready topic page so that developers can more easily learn about it.
To associate your repository with the binder-ready topic, visit your repo's landing page and select "manage topics."