Despite its groundbreaking success in the training of AI systems, it still remains an open research problem to provide a complete error analysis of Adam, not only for optimizing DNNs but even when applied to strongly convex stochastic optimization problems (SOPs). ^[1] It is the key contribution of this work to establish uniform a priori bounds for Adam and, thereby, to provide -- for the first time -- an unconditional error analysis for Adam for a large class of strongly convex SOPs. ^[2] The adaptive moment estimation (Adam) optimizer proposed by Kingma & Ba in 2014 is presumably the most popular stochastic gradient descent (SGD) optimization method for the training of deep neural networks (DNNs) in artificial intelligence (AI) systems. ^[3] The article presents six specific prayers designed to help believers express gratitude for their work and God's provision. ^[4] Previous error analysis results for strongly convex SOPs in the literature provide conditional convergence analyses that rely on the assumption that Adam does not diverge to infinity but remains uniformly bounded. ^[5] The fifth prayer thanks God for the rhythm of work and rest, asking for the ability to recharge and avoid burnout. ^[6] The first prayer is a thanksgiving for the opportunity to work and the ability to use natural strengths to serve others. ^[7] The third prayer expresses gratitude for unique talents and skills given by God to contribute to the world. ^[8]