Introduction
Explore t-SNE, a powerful technique for visualizing high-dimensional data in lower dimensions, widely used to reveal patterns and relationships in complex datasets.
Theory
Dive deep into the theoretical underpinnings of t-SNE, exploring how this powerful algorithm works, its mathematical formulation, and the intuition behind its success in visualizing high-dimensional data.
scikit-learn Example
Learn how to implement t-SNE using scikit-learn, a popular Python library for machine learning. This guide walks through the steps of applying t-SNE to visualize high-dimensional data, with detailed explanations and code examples.
TensorFlow Example
Prepare features with TensorFlow and visualize them with the maintained scikit-learn t-SNE implementation.
PyTorch Example
Prepare embeddings with PyTorch and visualize them with the maintained scikit-learn t-SNE implementation.
Common Mistakes and Best Practices
Avoid perplexity, preprocessing, reproducibility, interpretation, and API mistakes in t-SNE.
Comparison with Other Algorithms
A comprehensive comparison of t-SNE with other popular unsupervised learning algorithms, focusing on their strengths, weaknesses, and best use cases.