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

The video tutorial explores recurrent neural networks (RNNs) and their application in sequence modeling. It highlights the vanishing gradient problem in deep networks and introduces Long Short-Term Memory (LSTM) as a solution to maintain long-term dependencies. The tutorial also covers Gated Recurrent Units (GRU) and bidirectional RNNs, emphasizing their role in handling time-dependent information. Additionally, it discusses the attention mechanism and its integration into Transformers, which are pivotal in modern language models like BERT.

What are bidirectional recurrent neural networks and what advantages do they offer?

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How do attention mechanisms utilize bidirectional recurrent neural networks?

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Discuss the significance of Transformers in modern neural network architectures.

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