Long short-term memory

Long short-term memory network (LSTM) is a variation of recurrent neural network. It was proposed by the German researchers Sepp HochreiterSepp Hochreiter and Juergen SchmidhuberJuergen Schmidhuber as a solution to the vanishing gradient problem.

LSTM networks have internal long-term or short-term memory cells. The output of the LSTM network is modulated by these cells. LSTMs are needed for prediction of the neural networks. Predictions depend on the historical context of inputs not only on the very last input.

LSTMs hold information outside the normal flow of the recurrent neural network in its memory blocks or cells. The information can be stored in, written to or read from a cell as if it is data in a computer. The memory blocks are responsible for remembering things and manipulations and regulated by structures called gates. The gating mechanism contains three non-linear gates, input, output and forget gate.

LSTMs hold information outside the normal flow of the recurrent neural network in its memory blocks or cells. The information can be stored in, written to or read from a cell as if it is data in a computer. The memory blocks are responsible for remembering things and manipulations. LSTMs are implemented with element-wise multiplication by Sigmoids. It has the advantage of being differentiable and suited for backpropagation.

LSTMs are implemented with element-wise multiplication by Sigmoids layers output of one and zero. It has the advantage of being differentiable and suited for backpropagation.

LSTMs are used in text generation, handwriting recognition, handwriting generation, music generation, language translation and image captioning.

Long short-term memory network (LSTM) is a variation of recurrent neural network. It was proposed by the German researchers Sepp Hochreiter and Juergen Schmidhuber as a solution to the vanishing gradient problem.

LSTM networks have internal long-term or short-term memory cells. The output of the LSTM network is modulated by these cells. LSTMs are needed for prediction of the neural networks. Predictions depend on the historical context of inputs not only on the very last input.

LSTMs hold information outside the normal flow of the recurrent neural network in its memory blocks or cells. The information can be stored in, written to or read from a cell as if it is data in a computer. The memory blocks are responsible for remembering things and manipulations. LSTMs are implemented with element-wise multiplication by Sigmoids. It has the advantage of being differentiable and suited for backpropagation.