  We investigate generalized versions of the Iteratively Regularized Landweber
Method, initially introduced in [Appl. Math. Optim., 38(1):45-68, 1998], to
address linear and nonlinear ill-posed problems. Our approach is inspired by
the data-driven perspective emphasized in the introduction by Aspri et al.
[Numer. Funct. Anal. Optim., 41(10):1190-1227, 2020]. We provide a rigorous
analysis establishing convergence and stability results and present numerical
outcomes for linear operators, with the Radon transform serving as a prototype.
