際際滷shows by User: arjoly / http://www.slideshare.net/images/logo.gif 際際滷shows by User: arjoly / Wed, 27 May 2015 13:26:14 GMT 際際滷Share feed for 際際滷shows by User: arjoly The genesis of clusterlib - An open source library to tame your favourite supercomputer /slideshow/the-genesis-of-clusterlib/48657887 slides-150527132614-lva1-app6892
The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects. The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.]]>
The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects. The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.]]> Wed, 27 May 2015 13:26:14 GMT /slideshow/the-genesis-of-clusterlib/48657887 arjoly@slideshare.net(arjoly) The genesis of clusterlib - An open source library to tame your favourite supercomputer arjoly The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects. The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-150527132614-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentations tells the story of clusterlib an open source package from the problem statement to a first grade an open source library. Awesome tools are also presented for software projects. The goal of the clusterlib is to ease the creation, launch and management of embarrassingly parallel jobs on supercomputers with schedulers such as SLURM and SGE.
The genesis of clusterlib - An open source library to tame your favourite supercomputer from Arnaud Joly
]]> 678 1 https://cdn.slidesharecdn.com/ss_thumbnails/slides-150527132614-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Numerical tour in the Python eco-system: Python, NumPy, scikit-learn /slideshow/slides-39833610/39833610 slides-141003042208-phpapp01
We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.]]>
We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.]]> Fri, 03 Oct 2014 04:22:08 GMT /slideshow/slides-39833610/39833610 arjoly@slideshare.net(arjoly) Numerical tour in the Python eco-system: Python, NumPy, scikit-learn arjoly We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-141003042208-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.
Numerical tour in the Python eco-system: Python, NumPy, scikit-learn from Arnaud Joly
]]> 3227 7 https://cdn.slidesharecdn.com/ss_thumbnails/slides-141003042208-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Git for (collaborative) writing /slideshow/slide-39468387/39468387 slide-140924060238-phpapp01
Git is an amazing source control version system for writing. It allows you keep track of the modification and collaborate with a large number of people. Platforms such as bitbucket or github make it straightforward to use.]]>
Git is an amazing source control version system for writing. It allows you keep track of the modification and collaborate with a large number of people. Platforms such as bitbucket or github make it straightforward to use.]]> Wed, 24 Sep 2014 06:02:38 GMT /slideshow/slide-39468387/39468387 arjoly@slideshare.net(arjoly) Git for (collaborative) writing arjoly Git is an amazing source control version system for writing. It allows you keep track of the modification and collaborate with a large number of people. Platforms such as bitbucket or github make it straightforward to use. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slide-140924060238-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Git is an amazing source control version system for writing. It allows you keep track of the modification and collaborate with a large number of people. Platforms such as bitbucket or github make it straightforward to use.
Git for (collaborative) writing from Arnaud Joly
]]> 805 5 https://cdn.slidesharecdn.com/ss_thumbnails/slide-140924060238-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 L1-based compression of random forest model際際滷 /slideshow/slide-39465674/39465674 slide-140924044642-phpapp02
Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. Link to the paper http://orbi.ulg.ac.be/handle/2268/124834]]>
Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. Link to the paper http://orbi.ulg.ac.be/handle/2268/124834]]> Wed, 24 Sep 2014 04:46:42 GMT /slideshow/slide-39465674/39465674 arjoly@slideshare.net(arjoly) L1-based compression of random forest model際際滷 arjoly Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. Link to the paper http://orbi.ulg.ac.be/handle/2268/124834 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slide-140924044642-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive, specially in the context of problems with very high-dimensional input spaces. We propose to study their compressibility by applying a L1-based regularization to the set of indicator functions defined by all their nodes. We show experimentally that preserving or even improving the model accuracy while significantly reducing its space complexity is indeed possible. Link to the paper http://orbi.ulg.ac.be/handle/2268/124834
L1-based compression of random forest model際際滷 from Arnaud Joly
]]> 1125 3 https://cdn.slidesharecdn.com/ss_thumbnails/slide-140924044642-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-arjoly-48x48.jpg?cb=1523635083 www.ajoly.org https://cdn.slidesharecdn.com/ss_thumbnails/slides-150527132614-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-genesis-of-clusterlib/48657887 The genesis of cluster... https://cdn.slidesharecdn.com/ss_thumbnails/slides-141003042208-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/slides-39833610/39833610 Numerical tour in the ... https://cdn.slidesharecdn.com/ss_thumbnails/slide-140924060238-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/slide-39468387/39468387 Git for (collaborative...