Deep Learning - Recurrent Neural Networks with TensorFlow - Paying Attention to Shapes

The video tutorial emphasizes the importance of understanding shapes in RNNs, focusing on size variables like N, T, D, M, and K. It guides through creating dummy data, setting up an RNN model, and making predictions. The tutorial also explores the weights in a simple RNN and provides a manual calculation of RNN outputs. A bonus exercise challenges viewers to modify the code for multiple samples.

What does the variable K indicate in an RNN model?

How do you initialize the initial hidden state in an RNN?

What is the process to calculate the hidden value H in an RNN?