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Long short-term memory (LSTM) units or blocks are part of a recurrent neural network structure. Recurrent neural networks are made to utilize certain types of artificial memory processes that can help these artificial intelligence programs to more effectively imitate human thought.
The recurrent neural network uses long short-term memory blocks to provide context for the way the program receives inputs and creates outputs. The long short-term memory block is a complex unit with various components such as weighted inputs, activation functions, inputs from previous blocks and eventual outputs.
The unit is called a long short-term memory block because the program is using a structure founded on short-term memory processes to create longer-term memory. These systems are often used, for example, in natural language processing. The recurrent neural network uses the long short-term memory blocks to take a particular word or phoneme, and evaluate it in the context of others in a string, where memory can be useful in sorting and categorizing these types of inputs. In general, LSTM is an accepted and common concept in pioneering recurrent neural networks.