RNN: Recurrent Neural Networks

In normal feed-forward neural networks the activation flows only in one direction, from the input layer to the output layer, eventually passing through a set of hidden layers. Conversely, recurrent neural networks (RNN) have also connections pointing backward, thus allowing them to take also the temporal dimension into account. This novel architecture enables them to take as their input not just the current input xi … Continue reading RNN: Recurrent Neural Networks

Predicting players’ departure in MMORPGs

MMORPG’s business model Nowadays, MMORPGs are so popular that their market is worth over a billion dollars in 2019 and it is expected to keep growing during the following years. They can count over 10 million active monthly players, and, as expected, their huge base of players is one the keys to their success. In fact, these kinds of games belong to the (FTP) Free-To-Play … Continue reading Predicting players’ departure in MMORPGs

Predicting players’ behaviors in MMORPGs

Have you ever heard the word “MMORPG“? Probably yes, but you don’t know its meaning, isn’t it? Well, then I’m going to tell you, it means Massive Multiplayer Online Role Play Games. They are that kind of videogames where each user controls a digital avatar and interacts with other online users in a digital world. You probably have already heard of many of them such … Continue reading Predicting players’ behaviors in MMORPGs

CNN: Convolutional Neural Networks

If we want machines to think, we need to teach them to see Fei-Fei Li Computer vision is one of the most challenging and fascinating field of machine learning. We can say it acquired popularity back in 2010 since the ILSVRC (Large Scale Visual Recognition Challenge) competition was introduced by Alex Berg from Stony Brook, Jia Deng from Princeton & Stanford, and Fei-Fei Li from … Continue reading CNN: Convolutional Neural Networks

Logistic regression

Logistic regression is a supervised machine learning model used for classification tasks. It works by learning an hyperplane defined by some coefficients (or weights) θ = [θ0,…,θd] such that it can split the data into two subsets according to their labels. These coefficients θ are composed by a weights vector w = [w1,…,wd] plus a bias term b. For simplicity, we will consider a binary … Continue reading Logistic regression