We apply three different deep learning models to reproduce state-of-the-art results in single image super resolution.
We’re thrilled today to announce the latest version of Neptune: Machine Learning Lab. This release will allow data scientists using Neptune to take some giant steps forward. Here we take a quick look at each of them.
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
Everybody who watched ‘Minority Report’ daydreams about crime forecasting in the real world. We have good news: machine learning algorithms can do that!
Deep learning vs human perception: creating a log loss benchmark for industrial & medical image classification problems (e.g. cancer screening).
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.
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.
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?
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.
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, …