ML without tears

Category: Calculus

  • Understanding the adjoint method via backpropagation

    In this post, we shed some light on the adjoint state method as used in the famous “Neural ODE” paper [1]. In Section 1, we start by introducing the adjoint state method in its raw form (ODE, loss minimization, adjoint equations), in continuous time (denoted by [C]). If this is already clear to you, then… no…

  • The plumber’s secret weapon: The Divergence Theorem

    This post explores the Gauss’s divergence theorem through intuitive and visual reasoning. To engage the reader’s imagination, we use water flux as our running example, although the reasoning applies to any vector field, e.g., electric, magnetic, heat or gravity field. Moreover, to keep things simple we work on the two dimensions, although the same principles…