Crime forecasting – ‘Minority Report’ realized

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!

How to create a product recognition solution

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

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

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

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

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.

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 […]