This paper analyzes how the topology and complexity of neural network embedding spaces evolve through layers, using algebraic topology tools like persistent homology and Betti numbers. Experiments across architectures like VGG, ResNet, DenseNet, and datasets demonstrate that as depth increases, topological complexity decays, which provides insights into model generaliza...