Explore a comprehensive lecture on the modern perspective of the connection between neural networks and kernels. Delve into fundamental questions, empirical observations on training loss and generalization, and over-parameterization in neural networks. Examine trajectory-based analysis, kernel matrices, and the main theory behind zero training error. Investigate empirical results on generalization, convolutional neural tangent kernels, and their application to CIFAR-10. Understand global and local average pooling, and explore experiments on UCI datasets and few-shot learning. Discover graph neural tangent kernels for graph classification, and gain insights into the latest research and references in this field.