How do Kernel Regularizers work with neural networks?

Regularization is the process of fine-tuning neural network models by inducing a penalty term in the error parameter to obtain an optimal and reliable model which converges better with minimal loss during testing and performs better for unseen data. Regularization helps us get a more generic and reliable model which functions well with respect to changes in patterns of data and any possible uncertainties. So in this article let us see how kernel regularizers work with neural networks and place at what layers of the neural networks are useful to obtain optimal neural networks. Regularization is the process of adding penalty factors to the network layers to alter the weight propagation through the layers which facilitate the model to converge optimally. There are mainly two types of penalties that can be enforced on the network layers which are named as L1 regularization considers the weight of the layers as it is while the L2 regularization considers the squares of weights.