recurrent neural networks the following points are to be noted the most important element of the lstm is the cell statedenoted by (ti (tz (tf (tc ( the cell state is updated based on the block input (tand the previous cell state ( the input gate (tdetermines which fraction of the block input makes it into the cell state (hencecalled gatethe forget gate (tdetermines how much of the previous cell state to retain the output yt is determined based on the cell state (tand the output gate ( )which determines how much the cell state affects the output the (ttermreferred to as the block inputproduces value based on the current input and the previous output the (ttermreferred to as the input gatedetermines how much of the input to retain in the cell state ( all the terms are peephole connections that allow for faction of the cell state to factor into the computation of the term in question the computation of the cell state (idoes not encounter the issue of the vanishing gradient (this is referred to as the constant error carousal howeverlstms are affected by exploding gradientsand gradient clipping is used while training