Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Deep RNNs Solution

The video tutorial explains the structure and function of neural networks, focusing on inputs, weight matrices, and layers. It introduces the concept of recurrent layers and discusses the depth of neural networks, particularly in deep recurrent neural networks (RNNs). The tutorial also covers weight sharing across time in these networks, emphasizing the importance of layer-specific weights and their role in deep learning.

Describe the role of inputs in the structure of a deep recurrent neural network.

What defines a recurrent neural network as being 'deep'?

Explain the concept of unrolling depth in time in the context of recurrent neural networks.

How do the weights differ across layers in a deep recurrent neural network?

What is the significance of weight sharing in deep recurrent neural networks?