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Related Questions
- What is the main goal of t-SNE algorithm in dimensionality reduction?
- How does t-SNE differ from other dimensionality reduction techniques like PCA and t-SVD?
- What are the advantages of using t-SNE for word embedding visualization?
- Can you explain the concept of perplexity in t-SNE and how it affects the visualization?
- How does t-SNE handle high-dimensional data and what are the challenges associated with it?
- What are some common applications of t-SNE in natural language processing and word embedding visualization?
- How does t-SNE compare to other word embedding visualization techniques like UMAP and Word2Vec?
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