Deep Neural Networks for Physicists

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Abstract

Neural networks can be trained to perform many challenging tasks, including image recognition and natural language processing, just by showing them many examples. While neural networks have been introduced already in the 50s, they really have taken off in the past decade, with spectacular successes in many areas. Often, their performance now surpasses humans, as proven by the recent achievements in handwriting recognition and in winning the game of ‘Go’ against expert human players. They are now also being considered more and more for applications in physics, ranging from predictions of material properties to analyzing phase transitions. In thesis the student will be introduced to the theoretical framework of Deep neural networks and will tackle some real world problem using Python libraries for neural networks.

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LIPh
Laboratory of Interdisciplinary Physics