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Podcast: Machine Learning Guide
Episode:

23. Deep NLP 2

Category: Technology
Duration: 00:42:45
Publish Date: 2017-08-20 18:23:46
Description:

RNN review, bi-directional RNNs, LSTM & GRU cells.

## Resources - Overview Articles: ** Unreasonable Effectiveness of RNNs (http://karpathy.github.io/2015/05/21/rnn-effectiveness/) `article:easy` ** Deep Learning, NLP, and Representations (http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) `article:medium` ** Understanding LSTM Networks (http://colah.github.io/posts/2015-08-Understanding-LSTMs/) `article:medium` - Stanford cs224n: Deep NLP (https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) `course:medium` (replaces cs224d) - TensorFlow Tutorials (https://www.tensorflow.org/tutorials/word2vec) `tutorial:medium` (start at Word2Vec + next 2 pages) - The usual DL resources (pick one): ** Deep Learning Book (http://amzn.to/2tXgCiT) (Free HTML version (http://www.deeplearningbook.org/)) `book:hard` comprehensive DL bible; highly mathematical ** Fast.ai (http://course.fast.ai/) `course:medium` practical DL for coders ** Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) `book:medium` shorter online "book"

## Episode

RNN Review ** Vanilla: When words + running context is sufficient. ** POS, NER, stocks, weather ** Bidirectional RNN (BiLSTM): When stuff from right helps too ** Encoder/decoder or Seq2seq: When you should hear everything first / spin a different way ** Classification, sentiment, translation ** Now w/ word embeddings

Train: backprop through time ** Vanishing/exploding gradient

LSTMs (http://colah.github.io/posts/2015-08-Understanding-LSTMs/) ** ReLU vs Sigmoid vs TanH (Nonlinearities future episode) ** Forget gate layer ** Input gate layer: decides which values to update ** Tanh layer: creates new candidate values ** Output layer

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