Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Programming.
model. Always evaluate your model. Be aware of the right balance between specificity and generalization. Monitor the behavior of the error or metrics to stop early the training. Keep a test set. Prevent overfitting with cross-validation on your partitioned dataset. Knowledge is all about sharing.