This lecture explores the concept of gradient in relation to the geometry of a function, focusing on descent directions and the properties they exhibit. It delves into the theoretical arguments provided by Taylor's theorem, illustrating how following a descent direction can lead to a reduction in the function's value. The lecture also discusses the relationship between the gradient and convexity, highlighting how the gradient can provide insights into the convexity of a function.