A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, distributed deep learning for the JVM

The purpose of this post is to give students of neural networks an intuition about the functioning of recurrent networks and purpose and structure of a prominent variation, LSTMs. Recurrent nets are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, the spoken word, or numerical times series data emanating from sensors, stock markets and government agencies. They are arguably the most powerful type of neural network, applicable even to images, which can be decomposed into a series of patches and treated as a sequence. Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we'll make repeated analogies to memory in the brain.1 To understand recurrent nets, first you have to understand the basics of feedforward nets. Both of these networks are named after the way they channel information through a series of mathematical operations performed at the nodes of the network.