What's the difference between LSTM and Seq2Seq (M to 1)

Question

I want to ask the LSTM can be modeled as many-to-one. However, Seq2Seq can also be modeled as many-to-one. (M to N, when N is one). So, what is the difference?

Answer 1

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems). (https://en.wikipedia.org/wiki/Long_short-term_memory)

Seq2seq is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models and text summarization. ... Seq2seq turns one sequence into another sequence. It does so by use of a recurrent neural network (RNN) or more often LSTM or GRU to avoid the problem of vanishing gradient. (https://en.wikipedia.org/wiki/Seq2seq)

From my understanding, I guess Seq2Seq is a model which is optimized for NLP and uses an LSTM or GRU under the hood.