Convectional numerical methods and deep learning techniques are very relevant and widely used in computational fluid dynamics (CFD) to solve nonlinear differential equations. In this paper, we compare a recently developed cubic spline variation of interpolation technique with the deep learning technique called physics-informed neural networks (PINNs) on the Blasius equation which is a nonlinear differential equation that describes the boundary layer flow of fluids over a flat surface. Both techniques are seen to be comparable with the results from other classical methods where the equation is converted to an initial value problem and then solved.