The techniques used in this paper to study the properties of the Neural Tangent Kernel (NTK) can be applied to investigate the properties of other kernel functions associated with neural networks. Kernel functions play a crucial role in defining the learning dynamics and generalization behavior of neural networks, and understanding their properties is essential for optimizing network performance. By adapting the analysis and characterization techniques presented in this paper, researchers can explore the positivity, definiteness, and other key properties of different kernel functions used in neural networks, such as the Gram matrix kernel or the feature space kernel. This can provide valuable insights into the behavior and performance of various neural network architectures beyond feedforward networks.