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.

What is the main focus of the lecture discussed in the text?

Explain the problem with using a regular feedforward ANN for word classification.

How does a recurrent neural network differ from a regular ANN in terms of input processing?

What does the term 'hidden states' refer to in the context of recurrent neural networks?

Describe the mathematical representation of the recurrence in a recurrent neural network.

Discuss the significance of the Fibonacci sequence in understanding recurrent neural networks.

What are the shapes of the weight and bias vectors in a recurrent neural network?