Every Neural Network Explained - LSTM - Long Short Term Memory Networks

Summary

The transcript explores the evolution of language models, specifically focusing on how Long Short-Term Memory (LSTM) networks solved the critical "vanishing gradient" problem faced by traditional Recurrent Neural Networks (RNNs). By introducing dedicated memory cells and enabling multi-layered network architectures, LSTMs can better handle long sentences and capture different levels of linguistic abstraction, with each layer processing words sequentially and generating increasingly complex representations. The key innovation is the ability to retain and retrieve important information across longer sequences, representing a significant advancement in natural language processing technology.