Visualizing Your Embeddings

An evolutionary guide from SNE to t-SNE and UMAP

Introduction

According to multiple estimates, 80% of data generated by businesses today is unstructured data such as text, images, or audio. This data has enormous potential for machine learning applications, but there is some work to be done before it can be used directly. Feature extraction helps extract information from the raw data into embeddings. Embeddings, which I covered in a previous piece with my co-author and …

data visualization deep-dives embedding snet umap