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

The video discusses the significance of recurrent neural networks (RNNs) and their ability to handle long-term dependencies. It compares Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), highlighting their differences in gate structures and parameter usage. The video explains the components of an LSTM unit, including the forget, update, and output gates, and discusses the complexity and flexibility of LSTMs compared to GRUs. It also introduces bidirectional RNNs, which process information from both past and future contexts to improve target prediction.

What are the advantages of using GRU over LSTM in certain scenarios?

Discuss the vanishing gradient problem and how LSTM addresses it.

Describe the concept of bidirectional recurrent neural networks.