ELEKTRONN - Convolutional Neural Network Toolkit in Python. Fast GPU acceleration and easy usage.

A configurable toolkit for 3D2D Convolutional Neural Networks CNN. CNNs are used in Machine Learning to solve difficult classification and regression tasks. ELEKTRONN is based on Theano and benefits from fast GPU implementations. This toolkit was created by Marius Killinger and Gregor Urban at the Max Planck Institute For Medical Research For Medical Research to solve connectomics tasks. We provide a ready-made flexible training pipeline and comprehensive documentation.

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Deep Learning

ICML 2013 Challenges in Representation Learning. 8230; moving beyond shallow machine learning since 2006! MILA is Hiring Two Software Engineers. Conference on the Economics of Machine Intelligence-Dec 15. Open Discussion of ICLR 2016 Papers is Now Open. Software Developer Position at MILA. A list of deep learning research groups and labs,. Discuss on our WP Forum.

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LTP Robotics-Make Robots Think-Birotic Ltd,. Gazebo robotics simulator with ROS. This tutorial is intended for roboticists that want to have realistic simulations of their robotic scenarios. Gazebo is a 3D simulator, while ROS serves as the interface for the robot. Combining both results in a powerful robot simulator. This Tutorial was tested with an Ubuntu 12. 10 with ROS Hydro and Gazebo-1.

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PAGE TITLE

ELEKTRONN - Convolutional Neural Network Toolkit in Python. Fast GPU acceleration and easy usage.

DESCRIPTION

A configurable toolkit for 3D2D Convolutional Neural Networks CNN. CNNs are used in Machine Learning to solve difficult classification and regression tasks. ELEKTRONN is based on Theano and benefits from fast GPU implementations. This toolkit was created by Marius Killinger and Gregor Urban at the Max Planck Institute For Medical Research For Medical Research to solve connectomics tasks. We provide a ready-made flexible training pipeline and comprehensive documentation.

CONTENT

This domain states the following, "Is a deep learning toolkit that makes powerful neural networks accessible to scientists outside of the machine learning community." We analyzed that the web site also said " Our focus lies on high troughput analysis of large scale 2D and 3D images with." It also stated " That must be provided initially. Once a good mapping has been found, it can be used to make predictions on new data. Membrane and mitochondria probability maps. Predicted with a CNN with recursive training. Zebra finch area X dataset j0126 by Jörgen Kornfeld."

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