advanced use of recurrent neural networks in this sectionwe'll review three advanced techniques for improving the performance and generalization power of recurrent neural networks by the end of the sectionyou'll know most of what there is to know about using recurrent networks with keras we'll demonstrate all three concepts on temperature-forecasting problemwhere you have access to timeseries of data points coming from sensors installed on the roof of buildingsuch as temperatureair pressureand humiditywhich you use to predict what the temperature will be hours after the last data point this is fairly challenging problem that exemplifies many common difficulties encountered when working with timeseries we'll cover the following techniquesrecurrent dropout--this is specificbuilt-in way to use dropout to fight overfitting in recurrent layers stacking recurrent layers--this increases the representational power of the network (at the cost of higher computational loadsbidirectional recurrent layers--these present the same information to recurrent network in different waysincreasing accuracy and mitigating forgetting issues temperature-forecasting problem until nowthe only sequence data we've covered has been text datasuch as the imdb dataset and the reuters dataset but sequence data is found in many more problems than just language processing in all the examples in this sectionyou'll play with weather timeseries dataset recorded at the weather station at the max planck institute for biogeochemistry in jenagermany in this dataset different quantities (such air temperatureatmospheric pressurehumiditywind directionand so onwere recorded every minutesover several years the original data goes back to but this example is limited to data from - this dataset is perfect for learning to work with numerical timeseries you'll use it to build model that takes as input some data from the recent past ( few daysworth of data pointsand predicts the air temperature hours in the future download and uncompress the data as followscd ~/downloads mkdir jena_climate cd jena_climate wget unzip jena_climate_ csv zip let' look at the data olaf kollewww bgc-jena mpg de/wetter licensed to