The video tutorial introduces Long Short Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN) that incorporates memory cells into hidden layer neurons. LSTMs are designed to learn long-term dependencies effectively, overcoming the limitations of traditional RNNs. First presented by Hochreiter and Schmidhuber in 1997, LSTMs have been improved and widely adopted due to their effectiveness in various applications. The tutorial highlights the advantages of LSTMs, such as their ability to retain information over long periods without significant effort.

What is the primary function of LSTM networks in neural networks?

Who were the first to present LSTM networks?

Why are LSTM networks frequently employed in various issues?

What issue do LSTM networks not suffer from, according to the text?

How do LSTM networks retain knowledge for lengthy periods?