In this article, we delve into the research paper titled 'The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks'. We put our focus on the significance of this study and evaluate its relevance in the current landscape of the theory of machine learning. This paper describes how solving a convex problem can directly give the solution to the highly non-convex problem that is optimizing a two-layer ReLU Network. After giving some intuition on the proof through a few examples, we will observe the limits of this model as we might not yet be able to throw away the non-convex problem.