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on 11-Jan-2023 (Wed)

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#deep-learning #keras #lstm #python #sequence

1.4.1 LSTM Weights

A memory cell has weight parameters for the input, output, as well as an internal state that is built up through exposure to input time steps. Input Weights. Used to weight input for the current time step. Output Weights. Used to weight the output from the last time step. Internal State. Internal state used in the calculation of the output for this time step

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#feature-engineering #lstm #recurrent-neural-networks #rnn
For natural language processing, an RNN would encode the sentence “A black cat jumped on the table” as a sequence of seven vectors (x 1 , x 2 , … x 7 ), where each word would be represented as a single non-zero value in a sparse vector 2 (Goodfellow et al., 2016). For instance, if we train a model with a vocabulary of 100,000 words, the first word “A” in the sentence would be encoded as a sparse vector of 100,000 numerical values, all equal to 0, except the first (corresponding to the word “A”), which would be equal to 1. The word “black” would be encoded as a sparse vector of 100,000 zero's, except the 12,853rd element (corresponding to the word “black”) equal to 1, etc
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#deep-learning #keras #lstm #python #sequence
Given that LSTMs operate on sequence data, it means that the addition of layers adds levels of abstraction of input observations over time. In effect, chunking observations over time or representing the problem at different time scales. ... building a deep RNN by stacking multiple recurrent hidden states on top of each other. This approach potentially allows the hidden state at each level to operate at different timescale — How to Construct Deep Recurrent Neural Networks, 2013
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