👉 Regularizer in machine learning refers to a penalty function that is added to the loss function of a neural network. This regularization helps to prevent overfitting, which occurs when the model becomes too complex and starts to learn patterns it's not designed for. Regularizers can be used to control how much the model learns from data, or what we call "regularization" in machine learning. Regularizers are often implemented as a term in neural network architectures, such as dropout layers or L1/L2
