What is the relationship between RNN and LSTM?

An LSTM (Long Short Term Memory) is a type of Recurrent Neural Network (RNN) , where the same network is trained through sequence of inputs across “time”. I say “time” in quotes, because this is just a way of splitting the input vector in to time sequences, and then looping through the sequences to train the network.

The units of an LSTM are used as building units for the layers of a RNN , often called an LSTM network. LSTMs enable RNNs to remember inputs over a long period of time. This is because LSTMs contain information in a memory, much like the memory of a computer.

RNN stands for *Recurrent Neural Networks* these are the first kind of neural network algorithm that can memorize or remember the previous inputs in memory. … LSTM includes a ‘memory cell’ that can maintain information in memory for long periods of time.8 Eyl 2021

RNN stands for *Recurrent Neural Networks* these are the first kind of neural network algorithm that can memorize or remember the previous inputs in memory. … LSTM includes a ‘memory cell’ that can maintain information in memory for long periods of time.8 Eyl 2021

The units of an LSTM are used as building units for the layers of a RNN , often called an LSTM network. LSTMs enable RNNs to remember inputs over a long period of time. This is because LSTMs contain information in a memory, much like the memory of a computer.

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning (DL). Unlike standard feedforward neural networks, LSTM has feedback connections.

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning (DL). Unlike standard feedforward neural networks, LSTM has feedback connections.