training deep leaning models establishing the baseline with standard choices the best place to start the modelling process is with baseline model with standard choices (based on literature or part experienceof architecture and algorithms--for instanceusing convolutional neural networks (cnnsfor images or long short-term memory (lstmnetworks for sequences (both topics will be covered in upcoming using rectified linear units (relusas activation units and batch stochastic gradient descent (sgdare also good choices to start with basicallythe baseline model establishes straw man on which to improve based on an analysis of the shortcomings building an automatedend-to-end pipeline having decided upon baseline modelit is of critical importance to build an end-to-endfully automated pipeline that includes training the model on the training setmaking predictions on the parameter tuning setand computing the metrics on both sets automation is extremely importantas it enables the practitioner to iterate quickly on new models by tweaking the model architecture and hyperparameters orchestration for visibility while building the end-to-end pipelineit' also good idea to put in the orchestration to visualize histograms of activationsgradientsmetrics on training and validation setsetc visibility into the model trainingweightsand performance can be quite useful when it comes to debugging unexpected behavior the key point is to build the automation and orchestration for visibility to begin with this will save lot of time and energy in the future