Deep learning for Collider Phenomenology

In this talk, I present the basic building blocks of Deep learning and some new state of art techniques that can be used in challenging problems we face at the Large Hadron Collider. As a demonstration,  we explore two different problems involving the Higgs boson characterization and how the data representation influences the impact of Deep learning models and how we can use these tools to help us in the task of finding new physics phenomena at the overwhelming background noise which plagues the Large Hadron Collider.