recurrent neural networks them from getting too large in figure - the gradient is clipped from overshooting and the cost function follows the dotted values rather than its original trajectory outside the desired region figure - gradient clipping long short-term memory let' take look at another variation on rnnsthe long short-term memory (lstmnetwork (see figure - the vanilla rnn had several trade-offs that led to poor performing networks in learning long dependencies between sequences in generalthe rnn is more prone towards noise and easily overfits while training they are also computationally very expensive to train lstms fit in perfectly to solve these problems by using more intuitive approach they are generally more robust to noise and capture shortas well long-term dependencies more accuratelywhile being easy to tune and trainas compared to rnns lstms also have faster computation speeds than rnns lstms have the gates that equip the handy functions that help the network remember long-term dependencies as well forget