How RNN Works?

Recurrent Neural Networks (RNNs) are designed to handle sequential data by retaining information from previous inputs in their internal states. This makes them ideal for tasks like language modeling and sequence prediction.

• Sequential Processing: RNN processes data step-by-step, keeping track of what has come before;
• Example: Sentence Completion: given the incomplete sentence "My favourite dish is sushi. So, my favourite cuisine is _____." the rnn processes the words one by one. After seeing "sushi", it predicts the next word as "Japanese" based on prior context;
• Memory in RNNs: at each step, the rnn updates its internal state (memory) with new information, ensuring it retains context for future steps;
• Training the RNN: rnn are trained using backpropagation through time (BPTT), where errors are passed backward through each time step to adjust weights for better predictions.

In summary, RNNs are powerful because they can remember and utilize past information, making them suitable for tasks that involve sequences.