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How to create a product recognition solution
/in Data science, Deep learning, Machine learning, Neptune /by Krzysztof Dziedzic and Patryk MiziułaIn 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 Data science, Deep learning, Machine learning /by Tomasz GrelIn 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
/in Data science, Deep learning, Machine learning /by Przemyslaw ChojeckiDespite 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 Data science, Deep learning, Machine learning, Neptune /by Krzysztof Dziedzic, Patryk Miziuła and Błażej OsińskiIn 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
/in Data science, Deep learning, Machine learning, Neptune /by Rafał HryciukAt 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
/in Data science, Deep learning, Machine learning /by Arkadiusz NowaczynskiWe describe 4th place solution based on image segmentation and deep learning for Dstl Satellite Imagery Feature Detection competition.
Training XGBoost with R and Neptune
/in Machine learning, Neptune /by Jan LasekWe 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 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 […]