{"id":28357,"date":"2016-09-07T18:49:07","date_gmt":"2016-09-07T15:49:07","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=28357"},"modified":"2021-03-10T13:21:20","modified_gmt":"2021-03-10T10:21:20","slug":"recurrent-neural-networks-classifying-diagnoses-with-long-short-term-memory","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/recurrent-neural-networks-classifying-diagnoses-with-long-short-term-memory\/","title":{"rendered":"Recurrent Neural Networks: Classifying Diagnoses with Long Short-Term Memory"},"content":{"rendered":"

\"tensorflow_los_angeles-v11\"<\/a><\/p>\n

When training a recurrent neural network, one can face a bunch of challenges. Long short-term memory<\/a> networks (LSTM) can come to the rescue, proving to be effective for learning from sequence data. At the recent TensorFlow meetup<\/a> in Los Angeles, the attendees learnt how to use an LSTM network for modeling clinic data.<\/p>\n

 <\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n