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klotz: t-sne* + pca*

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1. Mastering Customer Segmentation with LLM

   Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques

   2023-09-28 Tags: llm, segmentation, kmeans, k prototype, tsne, pca, sentence, embedding, clustering, exploratory data analysis by klotz
2. Visualizing Higher Dimensional Data Using t-SNE On TensorBoard | by Chiranjeevi Vegi | Medium

   2021-09-01 Tags: visualization, dimensionality reduction, t-sne, pca, tensorboard, data science by klotz
3. tSNE Degrades to PCA - Towards Data Science

   2020-04-07 Tags: pca, tsne, perplexity, visualization, clustering by klotz
4. The magic of t-SNE for visualizing your data features · Blog · Liip

   2020-01-16 Tags: t-sne, pca, machine learning, visualization by klotz
5. 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
6. 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
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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