Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Activity Many to One Solution

The video tutorial explains the varying lengths of outputs in machine translation, represented as Y0 hat, Y1 hat, etc. It covers the roles of decoder and encoder models, highlighting their connection. The loss function is defined by the outputs, with training labels in one hot vectors. The cross entropy loss is calculated for individual timestamps and summed for the final loss, similar to one-to-many and many-to-many cases.

What is the significance of one-hot vectors in the context of training labels?

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Describe how the cross-entropy loss function is applied to individual timestamps.

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