Deep Learning - Recurrent Neural Networks with TensorFlow - Recurrent Neural Networks (Elman Unit Part 2)

The video tutorial explains the use of RNNs for solving many-to-one and many-to-many tasks, such as spam detection and sentiment analysis. It discusses the architecture of RNNs, including the use of hidden states and shared weights. The tutorial also covers the concept of shapes and global max pooling, and how these relate to RNNs and 1D convolutions. Additionally, it explains the output shapes for different tasks and the possibility of stacking multiple RNN layers. Finally, it touches on the use of different RNN units in TensorFlow, including Elman RNN, GRU, and LSTM.

What is the significance of the final dense layer in RNN architectures?

In what scenarios would you keep all hidden states in an RNN?

How do the architectures of different RNN units remain consistent despite their differences?