How Neural Networks Learn

We wrote some of our own native neural network systems to analyze specific patterns in behavioral data.

How neural networks learn distributed representations

Deep learning’s effectiveness is often attributed to the ability of neural networks to learn rich representations of data.

By Garrett Hoffman in O'Reilly 

The concept of distributed representations is often central to deep learning, particularly as it applies to natural language tasks. Those beginning in the field may quickly understand this as simply a vector that represents some piece of data. While this is true, understanding distributed representations at a more conceptual level increases our appreciation of the role they play in making deep learning so effective.

To examine different types of representation, we can do a simple thought exercise. Let’s say we have a bunch of “memory units” to store information about shapes. We can choose to represent each individual shape with a single memory unit, as demonstrated in .... "