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.

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 […]

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 […]

Neptune 1.3 with TensorFlow integration and experiments in Docker

We’re happy to announce that a new version of Neptune became available this month. The latest 1.3 release of deepsense.ai’s machine learning platform introduces powerful new features and improvements. This release’s key added features are: integration with TensorFlow and running Neptune experiments in Docker containers.

Playing Atari on RAM with Deep Q-learning

In 2013 the Deepmind team invented an algorithm called deep Q-learning. It learns to play Atari 2600 games using only the input from the screen. Following a call by OpenAI, we adapted this method to deal with a situation where the playing agent is given not the screen, but rather the RAM state of the Atari machine. Our work was accepted to the Computer Games Workshop accompanying the IJCAI 2016 conference. This post describes the original DQN method and the changes we made to it. You can re-create our experiments using a publicly available code.

Neptune – Machine Learning Platform

deepsense.ai is releasing a new product. Neptune – a machine learning platform. Read about how we got the idea for Neptune and its main features.

Face recognition for right whales using deep learning

Which whale is it, anyway? Face recognition for right whales using deep learning

Right Whale Recognition was a computer vision competition organized by the NOAA Fisheries on the Kaggle.com data science platform. Our machine learning team at deepsense.ai has finished 1st! In this post we describe our solution.

Diagnosing diabetic retinopathy with deep learning

What is the difference between these 2 images? The one on the left has no signs of diabetic retinopathy, while the other one has severe signs of it. If you are not a trained clinician, the chances are, you will find it quite hard to correctly identify the signs of this disease.