Deep Learning with Python (Video 15)

The video tutorial introduces recurrent neural networks (RNNs) and their potential as universal computers capable of implementing any sequence-to-sequence mapping. It explains the dynamics of conventional RNNs, including state updates and non-linearities. The tutorial also covers modern architectures like LSTM and GRU, highlighting their differences and advantages over traditional RNNs. An implementation of RNNs using Theano is demonstrated, with a practical example and exercise to reinforce learning. The video concludes with a brief mention of future topics, including convolutional neural networks.

What is a key characteristic of recurrent neural networks that differentiates them from feedforward networks?

• They can only process fixed-size inputs.

• They do not use activation functions.

• They have no hidden layers.

• They can implement any sequence to sequence mapping.

What is the primary function of the forget gate in an LSTM?

• To initialize the network weights.

• To add noise to the input data.

• To determine how much of the previous cell state should be retained.

• To control the output of the network.

How do LSTMs differ from conventional RNNs in terms of internal states?

• LSTMs do not use any internal states.

• LSTMs have two internal states, while RNNs have one.

• RNNs have more internal states than LSTMs.

• Both have the same number of internal states.

What is a key advantage of GRUs over conventional RNNs?

• GRUs are less efficient than RNNs.

• GRUs require more computational resources.

• GRUs cannot handle long-term dependencies.

• GRUs have a simpler architecture with fewer gates.

In Theano, why is the projection of the input plus the bias calculated outside the loop?

• To increase computational complexity.

• To ensure the loop runs indefinitely.

• To make the code harder to understand.

• To reduce the number of calculations inside the loop.

What is the purpose of swapping axes in Theano when working with RNNs?

• To change the data type of the input.

• To ensure the first dimension represents different samples.

• To increase the number of hidden layers.

• To remove the bias from the input data.

What is a suggested exercise to deepen understanding of LSTMs in Theano?

• Design a new type of RNN.

• Implement LSTMs in Theano.

• Create a new activation function.

• Implement a feedforward network.