Data Science Austria

Understanding the 3 most common loss functions for Machine Learning Regression

A loss function in Machine Learning is a measure of how accurately your ML model is able to predict the expected outcome i.e the ground truth. The loss function will take two items as input: the output value of our model and the ground truth expected value. The output of … Read moreUnderstanding the 3 most common loss functions for Machine Learning Regression

Parsing Structured Documents with Custom Entity Extraction

Let’s talk about parsing structured documents with entity extraction! There are lots of great tutorials on the web that explain how to classify chunks of text with Machine Learning. But what if, rather than just categorize text, you want to categorize individual words, like this: You can’t make me apologize … Read moreParsing Structured Documents with Custom Entity Extraction

Using Bayesian Games to Address the Exploration-Exploitation Dilemma in Deep Learning Systems

Artificial intelligence(AI) agents often operate in environments with partial or incomplete information. In those settings, agents are often forced to find a balance between exploring the environment or taking actions that yield an immediate reward. The exploration-exploitation dilemma is one of the fundamental frictions in modern AI systems particularly in … Read moreUsing Bayesian Games to Address the Exploration-Exploitation Dilemma in Deep Learning Systems

Exploring Airbnb prices in London: which factors influence price?

Project aims and background Airbnb is a home-sharing platform that allows home-owners and renters (‘hosts’) to put their properties (‘listings’) online, so that guests can pay to stay in them. Hosts set their own prices for their listings, and although Airbnb and other sites provide some general guidance, there are … Read moreExploring Airbnb prices in London: which factors influence price?

Learning to play snake at 1 million FPS

There’s a bit more logic required such as checking for collisions and food collection, resetting environments after death and checking snakes aren’t trying to move backwards — it was very fun thinking of ways to implement all this in a way that is vectorized across multiple environments. The end result of all … Read moreLearning to play snake at 1 million FPS

QA: Machine Learning & Data Science Hackathons

This is the final hackathon trilogy part. The first part was devoted to the motivation to participate in such events. The second part told about the organizer’s mistakes and their results. The final part provides answers to all the remaining questions, which wasn’t fit in the previous two parts. Tell … Read moreQA: Machine Learning & Data Science Hackathons