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Neptune machine learning platform: grid search, R & Java support
/in Data science, Deep learning, Machine learning, Neptune /by Rafał HryciukIn 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
/in Data science, Deep learning, Machine learning /by Tomasz GrelRegion 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 […]
GeoJson Operations in Apache Spark with Seahorse SDK
/in Big data & Spark, Seahorse /by Adam JakubowskiA few days ago we released Seahorse 1.4, an enhanced version of our machine learning, Big Data manipulation and data visualization product. This release also comes with an SDK – a Scala toolkit for creating new custom operations to be used in Seahorse. As a showcase, we will create a custom Geospatial operation with GeoJson […]
Scheduling Spark jobs in Seahorse
/in Big data & Spark, Seahorse /by Michal SzostekIn the latest Seahorse release we introduced the scheduling of Spark jobs. We will show you how to use it to regularly collect data and send reports generated from that data via email. Use case Let’s say that we have a local meteo station and the data from this station is uploaded automatically to Google […]
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.