Unsupervised Learning: Dimensionality Reduction with t-SNE | AIML End-to-End Session 49
на канале: Noble Transformation Hub Ai Consciousness ®️
Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?
Welcome to Session 49 of our End-to-End AIML series! In this session, we focus on t-SNE (t-Distributed Stochastic Neighbor Embedding), a powerful dimensionality reduction technique specifically designed for visualizing high-dimensional data in lower-dimensional spaces like 2D or 3D. t-SNE is widely used in data exploration, making it easier to identify clusters and patterns in complex datasets.
What You'll Learn:
Introduction to t-SNE: Understand the key concepts behind t-SNE and how it differs from other dimensionality reduction techniques like PCA.
How t-SNE Works: Learn how t-SNE preserves the local structure of data while mapping it into lower dimensions, making it ideal for visualizing complex data relationships.
Applications of t-SNE: Explore how t-SNE is used in real-world scenarios such as image recognition, natural language processing, and genomic data analysis.
Parameters and Tuning: Get insights into the important parameters of t-SNE (such as perplexity and learning rate) and how to tune them for optimal performance and visualization.
Hands-On Coding Example: Follow along with a step-by-step implementation of t-SNE in Python using Scikit-learn and Matplotlib. Visualize datasets such as MNIST, clustering points in a 2D space.
Limitations and Best Practices: Learn about the limitations of t-SNE, including computational cost and difficulty in interpreting the global structure of data, along with best practices for using it effectively.
This session is perfect for anyone looking to visualize and understand the hidden structures in their high-dimensional data using t-SNE.
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