Deep learning in Solar Physics

During the last decade, deep learning has emerged as a powerful tool to analyze the relevant information from observations. By exploiting some symmetries and patterns they can be optimized to perform faster and sometimes better than conventional methods. In this talk, I will present some examples of how we have successfully applied deep learning to several problems in Solar Physics and highlight some results related to image deconvolution, parameter inference, and noise reduction in observational data.