One of the major drawbacks of neural networks (or any machine learning model, in general) is the inability to handle data with huge dimensions, effectively. In this blog post, I will be exploring the technique of neural embedding, which is a variation on using auto-encoders for dimensionality reduction. Data with high dimensions pose a very unique problem to any statistical analyses as the volume of the vector space under consideration increases exponentially with increase in dimensionality. ...
