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Tags: dimensionality reduction* + python*

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1. Diving into Word Embeddings with EDA

   Exploratory data analysis (EDA) is a powerful technique to understand the structure of word embeddings, the basis of large language models. In this article, we'll apply EDA to GloVe word embeddings and find some interesting insights.

   2024-07-12 Tags: word, embeddings, eda, glove, pca, dimensionality reduction, nlp, text, python by klotz
2. Principal Component Analysis: The Dragon (Data Scientist) Warrior’s Secret Technique | by John Newcomb | Nov, 2021 | Medium

   2021-11-08 Tags: machine learning, pca, dimensionality reduction, python, tutorial, pandas by klotz
3. Deep Learning for Single Cell Biology – Towards Data Science

   2019-05-08 Tags: dimensionality reduction, tensorflow, tsne, pca, autoencoder, deep learning, biology, genetics, cells, rna by klotz
4. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction — umap 0.3 documentation

   2018-10-09 Tags: python, umap, dimensionality reduction, machine learning, documentation by klotz
5. GitHub - lmcinnes/umap: Uniform Manifold Approximation and Projection

   Alternative to t-SNE and PCA

   2018-10-09 Tags: umap, visualization, dimensionality reduction, python, embedding, machine learning, t-sne, pca by klotz
6. An Introduction to t-SNE with Python Example

   2018-08-16 Tags: t-sne, dimensionality reduction, python by klotz
7. 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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