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Looks like that job is now done.
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">From:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">"Foster, Ian T. via DL-interest" <<a href="mailto:dl-interest@lists.cels.anl.gov" class="">dl-interest@lists.cels.anl.gov</a>><br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Subject:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class=""><b class="">[DL-interest] The missing piece in deep learning?</b><br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Date:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">May 16, 2019 at 11:52:29 AM CDT<br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">To:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">"<a href="mailto:DL-interest@lists.cels.anl.gov" class="">DL-interest@lists.cels.anl.gov</a>" <<a href="mailto:DL-interest@lists.cels.anl.gov" class="">DL-interest@lists.cels.anl.gov</a>><br class="">
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<span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Reply-To:
</b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">"Foster, Ian T." <<a href="mailto:foster@anl.gov" class="">foster@anl.gov</a>><br class="">
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Have you ever lamented the lack of good DNN tools in Fortran? If so, you need lament no longer.
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<div class=""><a href="https://arxiv.org/pdf/1902.06714" class="">https://arxiv.org/pdf/1902.06714</a></div>
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<p class=""><span style="font-size: 21.000000pt; font-family: 'LinLibertineTB'" class="">A parallel Fortran framework for neural networks and deep learning </span></p>
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<p class=""><span style="font-size: 10.000000pt; font-family: 'LinLibertineT'" class="">This paper describes neural-fortran, a parallel Fortran frame- work for neural networks and deep learning. It features a simple interface to construct feed-forward neural
networks of arbitrary structure and size, several activation functions, and stochastic gradient descent as the default optimization algorithm. neural-fortran also leverages the Fortran 2018 standard collective subroutines to achieve data-based paral- lelism
on shared- or distributed-memory machines. </span></p>
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