{"id":6268,"date":"2020-05-25T17:38:02","date_gmt":"2020-05-25T15:38:02","guid":{"rendered":"https:\/\/igfae.usc.es\/~igfae-old\/the-virtual-course-on-neural-networks-applications-to-high-energy-physics-and-industry-started-today\/"},"modified":"2021-03-24T15:06:14","modified_gmt":"2021-03-24T13:06:14","slug":"the-virtual-course-on-neural-networks-applications-to-high-energy-physics-and-industry-started-today","status":"publish","type":"post","link":"https:\/\/igfae.usc.es\/~igfae-old\/the-virtual-course-on-neural-networks-applications-to-high-energy-physics-and-industry-started-today\/","title":{"rendered":"The virtual course on “Neural Networks: applications to high-energy physics and industry” started today"},"content":{"rendered":"

Today began the first session of the virtual course ‘Neural Networks: applications to high-energy physics and industry’,<\/a> a course organized by the Galician Institute of High Energy Physics (IGFAE) on the mornings of May 25, 26 and 27 and June 1, 2 and 3 in which these machine learning techniques used in research in particle physics will be introduced and whose applications extend far beyond academic research.<\/p>\n

Turning the course into virtual has allowed the participation of more than 60 people from different parts of the Peninsula, whose first three sessions are being taught by HSE University professor and researcher, Andrey Ustyuzhanin, data scientist and machine learning expert, who will make an introduction to neural networks this week. In the next, focused on its application to particle physics and industry, \u00c1lvaro Dosil from Triple Alpha and Miguel Vidal from \u00c1rtabro Tech, experts from CIT companies specialized in big data and artificial intelligence, will participate.<\/p>\n

Artificial neural networks (ANNs) are computational models capable of learning from previously provided data sets that constitute a different paradigm from that of classical computing algorithms in which tasks are executed sequentially. This ability enabled ANNs to produce accurate solutions to complex problems, such as image or sound recognition, that significantly outperform solutions based on classical computing. Its development was aided by rapid improvements in hardware capabilities in the past two decades and today is a highly current field of study.<\/p>\n

This course is part of the IGFAE academic training program for doctoral students as a complement to their training, although the courses are open to a wider public.<\/p>\n