Neptune machine learning platform: grid search, R & Java support

In February we released a new version of Neptune, our machine learning platform for data scientists, supporting them in more efficient experiment management and monitoring. The latest 1.4 release introduces new features, like grid search — a hyperparameter optimization method and support for R and Java programming languages. Grid Search The first major feature introduced […]

Region of interest pooling explained

Region of interest pooling (also known as RoI pooling) is an operation widely used in object detection tasks using convolutional neural networks. For example, to detect multiple cars and pedestrians in a single image. Its purpose is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7×7). We’ve just […]

Machine Learning for Applications in Manufacturing

Modern manufacturing technology is starting to incorporate machine learning throughout the production process. Predictive algorithms are being used to plan machine maintenance adaptively rather than on a fixed schedule. Meanwhile, quality control is becoming more and more automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject defects. In this post we […]

An internal validation leaderboard in Neptune

An internal validation leaderboard in Neptune

Internal 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

We’re happy to announce that a new version of Neptune became available this month. The latest 1.3 release of’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

Machine Learning Models Predicting Dangerous Seismic Events

Underground 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 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 is releasing a new product. Neptune – a machine learning platform. Read about how we got the idea for Neptune and its main features.

Euro 2016 Predictions Using Team Rating Systems

Euro 2016 Predictions Using Team Rating Systems

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.

Face recognition for right whales using deep learning

Which whale is it, anyway? Face recognition for right whales using deep learning

Right Whale Recognition was a computer vision competition organized by the NOAA Fisheries on the data science platform. Our machine learning team at has finished 1st! In this post we describe our solution.