Sequence Models

Sequence Models

1. Notation

   • X<t> - i-th record of the input sequence
   • Y<t> - i-th record of the output sequence
   • Tx - number of records in the input sequence
   • Ty - number of record in the output sequence
   • T_x^(i) - input sequence length for the training example i

2. NLP

   • Representing words
     • using Vocabulary
       • each word is mapped to number

3. RNN

   • Forward propagation through time
   • Backward Propagation through time

4. GRU (Gated Recurrent Unit)

   • C - memory cell
   • C<t> = a<t>
   • GRU Network uses Gates to flag if the new word should update output, so that it allows to keeps track of the relationships between words further away from each other

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