deep learning for text and sequences summary in this you learned the following techniqueswhich are widely applicable to any dataset of sequence datafrom text to timeserieshow to tokenize text what word embeddings areand how to use them what recurrent networks areand how to use them how to stack rnn layers and use bidirectional rnns to build more-powerful sequence-processing models how to use convnets for sequence processing how to combine convnets and rnns to process long sequences you can use rnns for timeseries regression ("predicting the future")timeseries classificationanomaly detection in timeseriesand sequence labeling (such as identifying names or dates in sentencessimilarlyyou can use convnets for machine translation (sequence-tosequence convolutional modelslike slicenet )document classificationand spelling correction if global order matters in your sequence datathen it' preferable to use recurrent network to process it this is typically the case for timeserieswhere the recent past is likely to be more informative than the distant past if global ordering isn' fundamentally meaningfulthen convnets will turn out to work at least as well and are cheaper this is often the case for text datawhere keyword found at the beginning of sentence is just as meaningful as keyword found at the end see licensed to