One type of RNN model is an LSTM. The precise implementation details of LSTM are not within the scope of this book. An LSTM is a special RNN architecture, which was originally conceived by Hochreiter and Schmidhuber in 1997. This type of neural network has been recently rediscovered in the context of deep learning, because it is free from the problem of vanishing gradients, and offers excellent results and performance. LSTM-based networks are ideal for prediction and classification of temporal sequences, and are replacing many traditional approaches to deep learning.
It's a hilarious name, but it means exactly what it sounds. The name signifies that short-term patterns aren't forgotten in the long-term. An LSTM network is composed ...