Category Archives: Neural Networks

In past posts, I’ve described how Recurrent Neural Networks (RNNs) can be used to learn patterns in sequences of inputs, and how the idea of unrolling can be used to train them. It turns out that there are some significant … Continue reading →

A few weeks ago, Google announced that it was open sourcing an internal system called TensorFlow that allows one to build neural networks, as well as other types of machine learning models. (Disclaimer: I work for Google.) Because TensorFlow is designed … Continue reading →

In past posts, I’ve described the geometry of artificial neural networks by thinking of the output from each neuron in the network as defining a probability density function on the space of input vectors. This is useful for understanding how … Continue reading →

So far on this blog, we’ve mostly looked at data in two forms – vectors in which each data point is defined by a fixed set of features, and graphs in which each data point is defined by its connections … Continue reading →

Artificial neural networks have been around for a long time – since either the 1940s or the 1950s, depending on how you count. But they’ve only started to be used for practical applications such as image recognition in the last … Continue reading →