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An internal validation leaderboard in Neptune
/in Data science, Deep learning, Machine learning, Neptune /by Patryk MiziułaInternal validation is a useful tool for comparing results of experiments performed by team members in any business or research task. It can also be a valuable complement of public leaderboards attached to machine learning competitions on platforms like Kaggle. In this post, we present how to build an internal validation leaderboard using Python scripts […]
Neptune 1.3 with TensorFlow integration and experiments in Docker
/in Data science, Deep learning, Machine learning, Neptune /by Rafał HryciukWe’re happy to announce that a new version of Neptune became available this month. The latest 1.3 release of deepsense.ai’s machine learning platform introduces powerful new features and improvements. This release’s key added features are: integration with TensorFlow and running Neptune experiments in Docker containers.
Machine Learning Models Predicting Dangerous Seismic Events
/in Data science, Machine learning /by Michał TadeusiakUnderground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangerous events is of utmost importance – and also a great challenge for data scientists and their machine learning models. This was the inspiration for the organizers of AAIA’16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines.
Playing Atari on RAM with Deep Q-learning
/in Data science, Deep learning, Machine learning /by Henryk MichalewskiIn 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.
Neptune – Machine Learning Platform
/in Data science, Deep learning, Machine learning, Neptune /by Rafał Hryciukdeepsense.ai is releasing a new product. Neptune – a machine learning platform. Read about how we got the idea for Neptune and its main features.
R Notebook and Custom R Operations in the new Seahorse release
/in Seahorse /by Jan LasekPresenting new features in Seahorse, Release 1.3 – custom operations in R and enhanced data exploration capabilities in an R Notebook.
Euro 2016 Predictions Using Team Rating Systems
/in Data science, Machine learning /by Jan LasekThe 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.
Santa’s stolen sleigh – Kaggle’s optimization competition
/in Data science /by Marek CyganIn this post we present the Xpress prize-winning solution to the Kaggle’s optimization competition “Santa’s Stolen Sleigh.”
Optimize Spark with DISTRIBUTE BY & CLUSTER BY
/in Big data & Spark /by Witold JędrzejewskiDistribute by and cluster by clauses are really cool features in SparkSQL. Unfortunately, this subjectremains relatively unknown to most users – this post aims to change that.
US Baby Names – Data Visualization
/in Big data & Spark, Seahorse /by Rafał HryciukA few days ago we released Seahorse 1.1, an enhanced version of our machine learning, Big Data manipulation and visualization product. Today, we will show you how the new version of Seahorse can be used for data mining and data visualization.