advanced deep-learning best practices summary in this you learned the followinghow to build models as arbitrary graphs of layersreuse layers (layer weight sharing)and use models as python functions (model templatingyou can use keras callbacks to monitor your models during training and take action based on model state tensorboard allows you to visualize metricsactivation histogramsand even embedding spaces what batch normalizationdepthwise separable convolutionand residual connections are why you should use hyperparameter optimization and model ensembling with these new toolsyou're better equipped to use deep learning in the real world and start building highly competitive deep-learning models licensed to