## A intuitive explanation of natural gradient descent

A term that sometimes shows up in machine learning is the "natural gradient". While there hasn't been much of a focus on using it in practice, »

A term that sometimes shows up in machine learning is the "natural gradient". While there hasn't been much of a focus on using it in practice, »

In my previous post about generative adversial networks, I went over a simple method to training a network that could generate realistic-looking images. However, there were »

Is it possible for a neural network to learn how to talk like humans? Recent advances in recurrent neural networks allow us to model a language, »

I've been experimenting with OpenAI gym recently, and one of the simplest environments is CartPole. The problem consists of balancing a pole connected with one joint »

There's been a lot of advances in image classification, mostly thanks to the convolutional neural network. It turns out, these same networks can be turned around »

What really is a model? In short, it's an agent's understanding of the surrounding environment. In Markov processes, a model can be represented as probabilities of »

So far, our policy has simply been to act greedily on some value function. What if we tried to learn the policy itself? We can represent »

It's been shown many times that convolutional neural nets are very good at recognizing patterns in order to classify images. But what patterns are they actually »

The problem with the methods covered earlier is that it requires a model. Oftentimes, the agent does not know how the environment works and must figure »

Let's say we've got a Markov Decision Process, and a policy π. How good is this policy? Well, one way to figure it out is by »

When solving reinforcement learning problems, there has to be a way to actually represent states in the environment. A Markov State is a bunch of data »

In the past, there have been two main kinds of machine learning. In supervised learning, the computer is given both data and labels, and is simply »

The paper A Neural Algorithm of Artistic Style detailed on how to extract two sets of features from a given image: the content, and and the »