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.

deepsense.io helps improve the UK’s satellite defence and security intelligence

deepsense.io helps improve the UK’s satellite defence and security intelligence

The deepsense.io team makes a significant contribution to the satellite imagery project hosted at Kaggle.com aimed at increasing the efficiency of the decision-making process for the UK’s defence and security.

Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune?

Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune?

deepsense.io congratulates Cloudera on acquiring Sense.io and their recent launch of the Cloudera Data Science Workbench. “Well done, but too late!” – says deepsense.io’s CEO Tomasz Kulakowski, and asks: “Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune – a DevOps platform for machine and deep learning experiments, which works with any Hadoop distribution (incl. Hortonworks, Cloudera and MapR) and any cloud provider (such as AWS, MS Azure, Google Cloud Platform) as well as available on-premises?”

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 […]

Machine Learning for Applications in Manufacturing

Modern manufacturing technology is starting to incorporate machine learning throughout the production process. Predictive algorithms are being used to plan machine maintenance adaptively rather than on a fixed schedule. Meanwhile, quality control is becoming more and more automated, with adaptive algorithms that learn to recognize correctly manufactured products and reject defects. In this post we […]

GeoJson Operations in Apache Spark with Seahorse SDK

A 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 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

An internal validation leaderboard in Neptune

Internal 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 […]