In 2013 the Deepmind team invented an algorithm called deep Q-learning. It learns to play Atari 2600 games using only the input from the screen. Following a call by OpenAI, we adapted this method to deal with a situation where the playing agent is given not the screen, but rather the RAM state of the Atari machine. Our work was accepted to the Computer Games Workshop accompanying the IJCAI 2016 conference. This post describes the original DQN method and the changes we made to it. You can re-create our experiments using a publicly available code.
deepsense.ai is releasing a new product. Neptune – a machine learning platform. Read about how we got the idea for Neptune and its main features.
The 2016 UEFA European Championship is about to kick-off in a few hours in France with 24 national teams looking to claim the title. In this post, we’ll explain how to utilize various football team rating systems in order to make Euro 2016 predictions.
Right Whale Recognition was a computer vision competition organized by the NOAA Fisheries on the Kaggle.com data science platform. Our machine learning team at deepsense.ai has finished 1st! In this post we describe our solution.
The lives of brave firemen are threatened during dangerous emergency missions while they try to save other people and their property. In this post I would like to share my experiences and winning strategy for the AAIA’15 Data Mining Competition: Tagging Firefighter Activities at a Fire Scene, in which I took first place.
What is the difference between these 2 images? The one on the left has no signs of diabetic retinopathy, while the other one has severe signs of it. If you are not a trained clinician, the chances are, you will find it quite hard to correctly identify the signs of this disease.