English: At initialization, an ensemble of wide neural networks is a zero-mean Gaussian process; during training (gradient descent on mean-square error), the ensemble evolves according to the neural tangent kernel. The converged ensemble is a Gaussian process whose mean is the ridgeless kernel regression estimator and whose variance vanishes on the training points.