The field of deep learning is moving towards a better understanding of the training dynamics of neural networks, with a focus on the development of new mathematical frameworks and tools. Recent work has explored the use of mean ODEs to study the behavior of residual networks, and the application of graph neural networks to complex transactional data. The use of nonlocal neural tangent kernels and non-trainable modification architectures are also being investigated for their potential to improve the performance and interpretability of neural networks. Notable papers in this area include: The Hidden Width of Deep ResNets, which provides a novel mathematical perspective on ResNets and establishes tight error bounds for their training dynamics. Representation Learning on Large Non-Bipartite Transaction Networks using GraphSAGE, which demonstrates the practical application of GraphSAGE to non-bipartite heterogeneous transaction networks and highlights its scalability and interpretability.