Help me understand the neural tangent kernel in a practical sense

So I couldn't follow the math too well from Jacot's 2018 paper, but from reading what I could and this blog post (or, what I could follow of it), I understood the following about the NTK (please correct me where I'm wrong):

• The fundamental principle is that as the width of a feedforward network tends to infinity, the model becomes equivalent to a linear model under a kernel called the NTK.
• This kernel is dependent on the parameters and therefore …

deeplearning kernel sense