Solving Atari games with distributed reinforcement learning
We present the result of research conducted at deepsense.ai, that focuses on distributing a reinforcement learning algorithm to train on a large CPU cluster
Crime forecasting – ‘Minority Report’ realized
Everybody who watched ‘Minority Report’ daydreams about crime forecasting in the real world. We have good news: machine learning algorithms can do that!
Human log loss for image classification
Deep learning vs human perception: creating a log loss benchmark for industrial & medical image classification problems (e.g. cancer screening).
How to create a product recognition solution
In this post we present how to approach product recognition through the example of our solution for the iMaterialist challenge announced by CVPR and Google.
Running distributed TensorFlow on Slurm clusters
In this post we show a way to run machine learning experiments with distributed TensorFlow on Slurm clusters. A simple CIFAR-10 example is also included.
Machine learning application in automated reasoning
Despite recent advances in deep learning, the way mathematics is done today is still much the same as it was 100 years ago. Isn’t it time for a change?
Region of interest pooling in TensorFlow – example
In the previous post we explained what region of interest pooling (RoI pooling for short) is. In this one, we present an example of applying RoI pooling in TensorFlow. We base it on our custom RoI pooling TensorFlow operation. We also use Neptune as a support in our experiment performance tracking.
Neptune 1.5 – Python 3 support, simplified CLI, compact view
At the end of April 2017, deepsense.ai released a new version of Neptune, the DevOps platform for data scientists. Neptune 1.5 introduces a range of new features and improvements, including support for Python 3, simplification of Neptune CLI, offline execution, …
Deep learning for satellite imagery via image segmentation
We describe 4th place solution based on image segmentation and deep learning for Dstl Satellite Imagery Feature Detection competition.
Training XGBoost with R and Neptune
We present the training of an XGBoost model and evaluation of the results in an example analysis of bank customer data, using R with Neptune.