deep learning for text and sequences this timeyou achieve up to validation accuracy not badcertainly much better than the simplernn network--that' largely because lstm suffers much less from the vanishing-gradient problem--and slightly better than the fully connected approach from even though you're looking at less data than you were in you're truncating sequences after timestepswhereas in you were considering full sequences but this result isn' groundbreaking for such computationally intensive approach why isn' lstm performing betterone reason is that you made no effort to tune hyperparameters such as the embeddings dimensionality or the lstm output dimensionality another may be lack of regularization but honestlythe primary reason is that analyzing the globallong-term structure of the reviews (what lstm is good atisn' helpful for sentiment-analysis problem such basic problem is well solved by looking at what words occur in each reviewand at what frequency that' what the first fully connected approach looked at but there are far more difficult naturallanguage-processing problems out therewhere the strength of lstm will become apparentin particularquestion-answering and machine translation wrapping up now you understand the followingwhat rnns are and how they work what lstm isand why it works better on long sequences than naive rnn how to use keras rnn layers to process sequence data nextwe'll review number of more advanced features of rnnswhich can help you get the most out of your deep-learning sequence models licensed to