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
Explore how to implement t-SNE using TensorFlow. This guide provides a step-by-step approach to visualizing high-dimensional data with TensorFlow, focusing on the application of t-SNE for dimensionality reduction.
Pytorch Example
Learn how to implement t-SNE using PyTorch, focusing on the key steps required to visualize high-dimensional data effectively. This guide walks through the process of building a t-SNE model from scratch using PyTorch, including data preparation, computation of pairwise distances, and optimization techniques.
Common Mistakes and Best Practices
Learn about common mistakes when implementing t-SNE and explore best practices to avoid these pitfalls. This article includes practical tips and code examples to ensure accurate and effective t-SNE visualizations.
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.