- The paper
- More details in my blog article
visualzing 20newsgroup data
- you can also play with the embedding interactively
label embedding from stackexchange.datascience (deep-learning as an example)
- you can also play with the embedding interactively
core.py
20newsgroup visualization
20newsgroup_train.py: train for 20newsgroup dataset20newsgroup_viz.py: visualization usingsklearn.manifold.TSNE20newsgroup_tensorboard_embedding.py: produce the embedding files for tensorboard projector, which is more interactive- you can also play with it here using trained embeddings
link prediction
link_prediction.py: train (including grid search) and test
stackexchange label visualization
stackexchange_train.py: train for the stackexchange label cooccurence graphstackexchange_label_embedding.py: produce the embedding files for tensorboard projector- you can also play with it here using trained embeddings

