Recent theoretical work has shown that under certain conditions, massively overparameterized neural networks are equivalent to kernel regressors with a family of kernels called Neural Tangent Kernels (NTKs). My work in this subject aims to better understand the properties of NTK for various network architectures and relate them to the inductive bias of real neural networks. In particular, I will argue that for input data distributed uniformly on the sphere NTK favors low-frequency predictions over high-frequency ones, potentially explaining why overparameterized networks can generalize even when they perfectly fit their training data. I will further discuss the behavior of NTK when data is distributed nonuniformly and show that NTK (with ReLU activation) is tightly related to the classical Laplace kernel, which has a simple closed-form. Finally, I will discuss our analysis of NTK for convolutional networks, which indicates that these networks are biased toward learning low frequency target functions with any higher frequencies concentrated in local regions. Overall, our results suggest that much insight about neural networks can be obtained from the analysis of NTK.