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<p style="margin-top:0;margin-bottom:0">Hello fellow Data Scientists,</p>
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<p style="margin-top:0;margin-bottom:0">I came across this paper today and I thought it might be of interest. It uses convolutional neural networks (a topic which is I think is outside the scope of our current course, but we can try to discuss in the future),
however, I think it still may be of interest to some of you. I've attached a link to the paper, as well as to an article that tries to explain convolutional neural networks in a bit of detail.</p>
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<p style="margin-top:0;margin-bottom:0">Essentially, the paper treats a 2D potential well as an image, and trains a neural network to determine the ground state energy of the system, completely by-passing any
<i>ab-initio</i> approach for the calculation (they still needed to do some <i>ab-initio</i> work in order to train the network).<br>
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<p style="margin-top:0;margin-bottom:0">Deep Learning and the Schrodinger Equation:</p>
<p style="margin-top:0;margin-bottom:0"><a href="https://arxiv.org/abs/1702.01361" class="OWAAutoLink" id="LPlnk777999" previewremoved="true">https://arxiv.org/abs/1702.01361</a></p>
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<p style="margin-top:0;margin-bottom:0">An intuitive guide to Convolutional Neural Networks:</p>
<p style="margin-top:0;margin-bottom:0"><a href="https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050" class="OWAAutoLink" id="LPlnk39835" previewremoved="true">https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050</a></p>
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<p style="margin-top:0;margin-bottom:0">Have a good week!</p>
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<p style="margin-top:0;margin-bottom:0">Cheers,</p>
<p style="margin-top:0;margin-bottom:0">David<br>
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