Google open sources Embedding Projector to make high-demiensional data more manageable
Google open sources Embedding Projector to make high-dimensional data more manageable
This morning, google announced that it was open sourcing its data viualization tool, Embedding projector. The tool will help machine learning researchers to visualize data without having to install and run TensorFlow.
Dimensionality, and vecors in general, is not someting that most of us find easyto understand. The problem is that we all live in a three-dimensional world. We are taught length, width and height, so we struggle to imagine what a forth, fifth or sixth dimension might look like – this is wh most of us found Christopher Nolan’s representation of additional dimenstions wonky in the movie intestellar.
Instead of thinking about dimensionality of the world as we know it, try to just think purely about data. If I asked you to compare two houses, you might start by making a list of criteria that differentiate the houses. This list could include color, size, roof style and yard shape. This model coud be considered a four-dimensional model.
You could choose to visualize this data in a table, or you could try to represent it as a picture. To get there, you would have to use vectors. For a simple four dimensional model of two houses,you could actually create a chart in PowerPoint using traditional X and Y axis measurements in addition to features like bubble size and bubble color.
For a significanly more complex model wih thousands of dimnesions, traditional tools start to break down. Ta’s wher Google’s embedding Projector comes in. If you have ever used Spotify’s Discover Weelky, you hav run head on into embeddings, you just didn’t know it. At th advanced machine learning level, vector mappings can represen the attributes of songs. Mapping an enire collection of music against the preferences of an individual listner eables users to get personalizd, accurate, music recommendations – something that just would not work in PowerPoin.