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Google has open sourced its Embedding Projector, a web application that gives developers a way to visualize data that's being used to train their machine learning systems. Embedding Projector is part of TensorFlow, the machine learning technology behind some popular Google services like image search, Smart Reply in Inbox and Google Translate. The company released TensorFlow to the open source community last year in order to spur more development activity in the field. In a technical paper, Google researchers described the Embedding Projector as an interactive visualization tool that developers can use to interpret machine-learning models that rely on what are known as "embeddings." "With the widespread adoption of ML systems, it is increasingly important for research scientists to be able to explore how the data is being interpreted by the models," Google engineer Daniel Smilkov said in Google's open source blog.

Let us start with the simple stuff - showing word similarity in a vector space.

Google Open Sources Data Visualization Tool for Machine Learning By Jaikumar Vijayan Posted 2016-12-12 Print Google's Embedding Projector web applications will give developers a way to visualize how well their machine learning systems are interpreting data. Google has open sourced its Embedding Projector, a web application that gives developers a way to visualize data that's being used to train their machine learning systems. Embedding Projector is part of TensorFlow, the machine learning technology behind some popular Google services like image search, Smart Reply in Inbox and Google Translate. The company released TensorFlow to the open source community last year in order to spur more development activity in the field. In a technical paper, Google researchers described the Embedding Projector as an interactive visualization tool that developers can use to interpret machine-learning models that rely on what are known as "embeddings."

This morning, Google announced that it was open sourcing its data visualization tool, Embedding Projector. The tool will help machine learning researchers to visualize data without having to install and run TensorFlow. Dimensionality, and vectors in general, is not something that most of us find easy to 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 why most of us found Christopher Nolan's representation of additional dimensions wonky in the movie Interstellar.

Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive visualization and interpretation of embeddings.