Dive into the world of Image Embeddings and Vector Analysis with this comprehensive 21-minute tutorial. Master essential concepts like OpenAI CLIP embeddings, T-SNE, UMAP, and MNIST for image clustering and duplicate detection. Follow along with a beginner-friendly Google Colab notebook to set up your Python environment, cluster MNIST images using pixel brightness, compare T-SNE and UMAP techniques, and leverage OpenAI CLIP embeddings for advanced image analysis. Gain practical skills in Computer Vision and Data Science, and explore additional resources to further your learning journey in AI and technological innovations.
CLIP, T-SNE, and UMAP - Master Image Embeddings and Vector Analysis
Overview
Syllabus
Introduction
Python Environment Setup
Clustering MNIST images using pixel brightness
T-SNE vs. UMAP
Clustering images using OpenAI CLIP embeddings
: Using OpenAI CLIP embeddings to detect duplicates or close duplicates
Conclusions
Taught by
Roboflow
