Data Science and Machine Learning (Theory and Projects) A to Z - Vanishing Gradients in RNN: LSTM Optional

The video tutorial explains Long Short-Term Memory (LSTM) networks with a focus on mathematical equations rather than pictorial representations. It covers the definition and role of C hat, the candidate memory cell, and the various gates in LSTM, including update, forget, and output gates. The tutorial also discusses the parameters involved in LSTM and compares them with Gated Recurrent Units (GRU), highlighting LSTM's flexibility and complexity. Additionally, it introduces the peephole connection, a variant of LSTM that uses previous C values, and concludes with a note on standard implementations.

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