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klotz: word2vec* + t-sne*

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1. t-SNE – Laurens van der Maaten

   t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets. We applied it on data sets with up to 30 million examples. The technique and its variants are introduced in the following papers:

   2021-12-01 Tags: t-sne, laurents van der maaten, google, machine learning, visualization, dimensionality reduction, plot, word2vec, word embedding by klotz
2. Visualizing Word2Vec Embeddings with tSNE | Scottergories

   2021-09-20 Tags: visualization, word2vec, embedding, t-sne, nlp, machine_learning by klotz
3. Word2Vec + UMAP

   2021-09-08 Tags: word2vec, umap, visualization, t-sne by klotz
4. “Learn TensorFlow, the Word2Vec model, and the TSNE algorithm using rock bands”

   2018-08-19 Tags: tensorflow, tsne, kmeans, word2vec, machine learning, clustering, classification by klotz
5. (9) What are the best free and easy tools for visualizing word vectors trained by word2vec? - Quora

   2016-05-19 Tags: t-sne, word embedding, word2vec, visualization, dimensionality reduction by klotz
6. word2vec-pride-vis/tsne.py at master · arnicas/word2vec-pride-vis

   I used a Python t-SNE library to reduce the 200 feature dimensions for each word to 2 dimensions and plotted them in matplotlib. I saved out the x/y coordinates for each word in the book, so that I can show those words on the graph as you mouse over the replaced (blue) words.

   2016-05-19 Tags: t-sne, similarity, dimensionality reduction, pca, python, word2vec, matplotlib by klotz

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