Encoder Decoder Architectures for RNNs #deeplearning #machinelearning

Key Takeaways

this sequence to sequence model had equal siiz inputs and outputs most applications out there don't have equal size inputs and outputs though like in the case of language translation a 10-word sentence in English may not have a 10-word German translation even in the case of text summarization the input is a set of sentences but the output by definition is a reduced set of sentences clearly to deal with this new set of problems we need another type of architecture that takes in an input sequence but outputs a sequence of different length from the input it exists and it's called the encoder decoder architecture I'm pretty sure you can predict the two parts of this architecture the first is the encoder it converts the sequence to a vector and the second part is the decoder that converts the vector to a sequence