automatic differentiation in deep learning generalized term the key takeaway from this is that automatic differentiation enables the computation of gradients for arbitrarily complex loss functions and is one of the key enabling technologies for deep learning you should internalize the concepts of automatic differentiation and how it differs from both symbolic and numerical differentiation in the next we will study some additional topics related to deep learning in more detailincluding performance metrics and model evaluationanalyzing overfitting and underfittingregularizationand hyperparameter tuning finallywe will combine all the foundational bits about deep learning we've covered so far into practical example that implements feed-forward neural networks for real-world dataset