conclusions point in geometric space of statesthey should be used preferentially over convnets in the case of sequences where patterns of interest aren' invariant by temporal translation (for instancetimeseries data where the recent past is more important than the distant pastthree rnn layers are available in kerassimplernngruand lstm for most practical purposesyou should use either gru or lstm lstm is the more powerful of the two but is also more expensiveyou can think of gru as simplercheaper alternative to it in order to stack multiple rnn layers on top of each othereach layer prior to the last layer in the stack should return the full sequence of its outputs (each input timestep will correspond to an output timestep)if you aren' stacking any further rnn layersthen it' common to return only the last outputwhich contains information about the entire sequence following is single rnn layer for binary classification of vector sequencesmodel models sequential(model add(layers lstm( input_shape=(num_timestepsnum_features))model add(layers dense(num_classesactivation='sigmoid') model compile(optimizer='rmsprop'loss='binary_crossentropy'and this is stacked rnn layer for binary classification of vector sequencesmodel models sequential(model add(layers lstm( return_sequences=trueinput_shape=(num_timestepsnum_features))model add(layers lstm( return_sequences=true)model add(layers lstm( )model add(layers dense(num_classesactivation='sigmoid')model compile(optimizer='rmsprop'loss='binary_crossentropy'the space of possibilities what will you build with deep learningrememberbuilding deep-learning models is like playing with lego brickslayers can be plugged together to map essentially anything to anythinggiven that you have appropriate training data available and that the mapping is achievable via continuous geometric transformation of reasonable complexity the space of possibilities is infinite this section offers few examples to inspire you to think beyond the basic classification and regression tasks that have traditionally been the bread and butter of machine learning 've sorted my suggested applications by input and output modalities note that quite few of them stretch the limits of what is possible--although model could be trained on all of these tasksin some cases such model probably wouldn' generalize far from its training data sections and will address how these limitations could be lifted in the future licensed to