Image by Joshua Sukoff One of the most crucial hyperparameters in any machine learning (ML) model is the learning rate. A small learning rate often results in longer training times and can lead to overfitting. Conversely, a large learning rate may accelerate initial training but risks hindering the model’s convergence to the global minimum and can even cause divergence. Therefore, selecting the appropriate learning rate is a critical step in training any ML model.