Deep Learning - Recurrent Neural Networks with TensorFlow - Recurrent Neural Networks (Elman Unit Part 1)

The video introduces recurrent neural networks (RNNs), focusing on the Elman unit. It explains the importance of context in word classification tasks and how RNNs address this by using hidden representations from previous time steps. The lecture covers the mathematical foundation of RNNs, including weights and biases, and discusses ways to simplify recurrence equations. The content is designed to build intuition for RNNs, especially for those unfamiliar with engineering diagrams.

Why might a regular feedforward neural network struggle with word classification tasks?

• It cannot process numerical data.

• It lacks the ability to consider context.

• It requires too much computational power.

• It is too complex to implement.

What is a key feature of a recurrent neural network?

• It processes data in parallel.

• It uses previous hidden states for current predictions.

• It is only used for image processing.

• It requires labeled data for training.

What does the term 'unrolled RNN' refer to?

• A representation of RNNs showing each time step.

• A network that processes data in reverse order.

• A simplified version of a feedforward network.

• A network with no hidden layers.

In the context of RNNs, what does the expression 'W transpose X + b' represent?

• The process of data normalization.

• The calculation of output probabilities.

• The transformation of inputs using weights and biases.

• The initialization of network parameters.

What is the purpose of using different weights (WH and WX) in RNNs?

• To reduce the size of the network.

• To simplify the training process.

• To differentiate between input and hidden state transformations.

• To increase the speed of computation.

Why is it conventional to use a D by M matrix for WX in RNNs?

• It simplifies the mathematical notation.

• It aligns with the input size by output size convention.

• It reduces the number of parameters.

• It is required for backpropagation.

How can the recurrence in RNNs be simplified?

• By using only one type of activation function.

• By increasing the number of hidden layers.

• By using a single combined weight matrix.

• By removing all bias terms.