Although there is no specific batch size or learning rate that works for all models, we can find the best values for them by experimenting with multiple training instances. The primary step is to experiment with a set of batch size values and learning rates with the model. Observe the efficiency of the model by evaluating additional parameters such as Precision, Recall, and F1 Score. Test scores alone don't confirm the model's performance. Also, parameters such as Precision, Recall, and F1 Score vary according to the use case. You need to analyze your problem statement to get an idea about this. In this recipe, we will walk through key steps to determine the right batch size and learning rates.