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

The video tutorial explains the attention mechanism in machine translation, focusing on the mathematical details and workings of bidirectional networks. It covers the flow of activations in these networks and the role of the decoder network in producing translations. The tutorial also delves into the technical aspects of weight calculations in the attention model, including constraints and the use of softmax for normalization.

Discuss the significance of the weighted average in the context of the attention mechanism.

What technical details are provided about the weights in the attention model?

What are the constraints applied to the alpha values in the attention mechanism?