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Annotation 7753793932556

#feature-engineering #lstm #recurrent-neural-networks #rnn
Each module in the sequence is sometimes referred to as timesteps based on their position in the sequence. The RNN processes a sequence of input vectors (x 1 ,x 2 ,x 3 , …,x T ), with each vector being input into the RNN model at its corresponding timestep or position in the sequence.
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Each module in the sequence is sometimes referred to as timesteps based on their position in the sequence. The RNN processes a sequence of input vectors (x 1 ,x 2 ,x 3 , …,x T ), with each vector being input into the RNN model at its corresponding timestep or position in the sequence. The RNN has a multidimensional hidden state, which summarizes task-relevant information from the entire history and is updated at each timestep as well.

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