# t-SNE (t-Distributed Stochastic Neighbor Embedding)

**Overview:**

* **t-SNE** is a non-linear dimensionality reduction technique primarily used for visualization.
* It converts high-dimensional Euclidean distances into conditional probabilities that represent similarities.
* The algorithm minimizes the Kullback-Leibler divergence between these probability distributions in the high-dimensional and low-dimensional space.

**Key Characteristics:**

* Effective at creating a visual representation of complex data, revealing clusters and patterns.
* Sensitive to parameters such as perplexity and learning rate.
* Computationally intensive, especially for large datasets.

**Applications:**

* Visualizing high-dimensional data such as images, text, and gene expression data.
* Exploring and understanding the structure of the data.

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