as alwaysdeep learning is more an art than science we can provide guidelines that suggest what is likely to work or not work on given problembutultimatelyevery problem is uniqueyou'll have to evaluate different strategies empirically there is currently no theory that will tell you in advance precisely what you should do to optimally solve problem you must iterate wrapping up here' what you should take away from this sectionas you first learned in when approaching new problemit' good to first establish common-sense baselines for your metric of choice if you don' have baseline to beatyou can' tell whether you're making real progress try simple models before expensive onesto justify the additional expense sometimes simple model will turn out to be your best option when you have data where temporal ordering mattersrecurrent networks are great fit and easily outperform models that first flatten the temporal data to use dropout with recurrent networksyou should use time-constant dropout mask and recurrent dropout mask these are built into keras recurrent layersso all you have to do is use the dropout and recurrent_dropout arguments of recurrent layers stacked rnns provide more representational power than single rnn layer they're also much more expensive and thus not always worth it although they offer clear gains on complex problems (such as machine translation)they may not always be relevant to smallersimpler problems bidirectional rnnswhich look at sequence both waysare useful on naturallanguage processing problems but they aren' strong performers on sequence data where the recent past is much more informative than the beginning of the sequence there are two important concepts we won' cover in detail hererecurrent attention and sequence masking both tend to be especially relevant for natural-language processingand they aren' particularly applicable to the temperature-forecasting problem we'll leave them for future study outside of this book note licensed to