backpropagation gradient descent variants gradient-based optimization techniques practical implementation with pytorch summary automatic differentiation in deep learning numerical differentiation symbolic differentiation automatic differentiation fundamentals implementing automatic differentiation summary training deep leaning models performance metrics classification metrics regression metrics data procurement splitting data for trainingvalidationand testing establishing the achievable limit on the error rate establishing the baseline with standard choices building an automatedend-to-end pipeline orchestration for visibility analysis of overfitting and underfitting hyperparameter tuning model capacity regularizing the model early stopping norm penalties