understanding recurrent neural networks let' add to this picture an additional data flow that carries information across timesteps call its values at different timesteps ctwhere stands for carry this information will have the following impact on the cellit will be combined with the input connection and the recurrent connection (via dense transformationa dot product with weight matrix followed by bias add and the application of an activation function)and it will affect the state being sent to the next timestep (via an activation function an multiplication operationconceptuallythe carry dataflow is way to modulate the next output and the next state (see figure simple so far output - - output ct ct state input - figure output + + output_t activationwo*input_t uo*state_t vo*c_t bocarry track ct state + input input + going from simplernn to an lstmadding carry rack now the subtletythe way the next value of the carry dataflow is computed it involves three distinct transformations all three have the form of simplernn celly activation(dot(state_tudot(input_twbbut all three transformations have their own weight matriceswhich you'll index with the letters ifand here' what you have so far (it may seem bit arbitrarybut bear with melisting pseudocode det ails of he lstm archit ect ure ( output_t activation(dot(state_tuodot(input_twodot(c_tvoboi_t activation(dot(state_tuidot(input_twibif_t activation(dot(state_tufdot(input_twfbfk_t activation(dot(state_tukdot(input_twkbkyou obtain the new carry state (the next c_tby combining i_tf_tand k_t listing pseudocode det ails of he lstm archit ect ure ( c_t+ i_t k_t c_t f_t add this as shown in figure and that' it not so complicated--merely tad complex licensed to