if different features take values in different ranges (heterogeneous data)then the data should be normalized you may want to do some feature engineeringespecially for small-data problems once your tensors of input data and target data are readyyou can begin to train models developing model that does better than baseline your goal at this stage is to achieve statistical power that isto develop small model that is capable of beating dumb baseline in the mnist digit-classification exampleanything that achieves an accuracy greater than can be said to have statistical powerin the imdb exampleit' anything with an accuracy greater than note that it' not always possible to achieve statistical power if you can' beat random baseline after trying multiple reasonable architecturesit may be that the answer to the question you're asking isn' present in the input data remember that you make two hypothesesyou hypothesize that your outputs can be predicted given your inputs you hypothesize that the available data is sufficiently informative to learn the relationship between inputs and outputs it may well be that these hypotheses are falsein which case you must go back to the drawing board assuming that things go wellyou need to make three key choices to build your first working modellast-layer activation--this establishes useful constraints on the network' output for instancethe imdb classification example used sigmoid in the last layerthe regression example didn' use any last-layer activationand so on loss function--this should match the type of problem you're trying to solve for instancethe imdb example used binary_crossentropythe regression example used mseand so on optimization configuration--what optimizer will you usewhat will its learning rate bein most casesit' safe to go with rmsprop and its default learning rate regarding the choice of loss functionnote that it isn' always possible to directly optimize for the metric that measures success on problem sometimes there is no easy way to turn metric into loss functionloss functionsafter allneed to be computable given only mini-batch of data (ideallya loss function should be computable for as little as single data pointand must be differentiable (otherwiseyou can' use backpropagation to train your networkfor instancethe widely used classification metric roc auc can' be directly optimized hencein classification tasksit' common to optimize for proxy metric of roc aucsuch as crossentropy in generalyou can hope that the lower the crossentropy getsthe higher the roc auc will be table can help you choose last-layer activation and loss function for few common problem types licensed to