The neural tangent kernel (NTK) was created in the context of using the limit idea to study the theory of neural network. NTKs are defined from neural network models in the infinite-width limit trained by gradient descent. Such over-parameterized models achieved good test accuracy in experiments, and the success of the NTK emphasizes not only the importance of describing neural network models in the width limit of \(h \to \infty\) , but also the further development of deep learning theory for gradient flow in the step limit of \(\eta \to 0\) . And NTK can be widely used in various machine learning models. This review provides a comprehensive overview of the entire development of NTKs. Firstly, the bias–variance tradeoff in statistics, the popular over-parameterization and gradient descent in deep learning, and the widely used kernel method were introduced. Secondly, the development of research on the infinite-width limit in networks and the introduction of the concept of the NTK were introduced, and the development and latest progress of NTK theory were discussed. Finally, the researches on the migrations of NTKs to neural networks of other structures and the applications of NTKs to other fields of machine learning were presented.
