Starting deep learning hands-on: image classification on CIFAR-10

Tired of overly theoretical introductions to deep learning? Experiment hands-on with CIFAR-10 image classification with Keras by running code in Neptune.

Image classification sample solution for Kaggle competition

We’ve open sourced image classification sample solution that lets data scientists start competing in the currently running Kaggle Cdiscount competition.

Logo detection and brand visibility analytics

Monitoring brand visibility is an important business problem. We describe our solution for logo detection and visibility analytics with deep learning.

Fall 2017 release – launching Neptune 2.1 today!

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.

Solving Atari games with Distributed Reinforcement Learning

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

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.

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, …

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 […]