Neural Networks that Remember Sequences, RNN

An RNN (Recurrent Neural Network) is a type of neural network that processes input and output by focusing on the order of data.

Unlike traditional neural networks that treat each input independently, an RNN retains information from previous inputs to inform the current processing step.

Data where the order of each element is important is known as Sequential Data, and particularly, a set of values arranged in temporal order is referred to as Time Series Data.

RNNs are mainly used for sequential data where the order of inputs is critical.

Here are some examples:

• Sentences: The meaning of a sentence is completed when characters or words are sequentially connected.

• Speech: The order of sounds must be considered to understand the meaning of speech.

• Stock Price Analysis: When predicting stock prices that change over time, past data points are utilized.

RNN is advantageous for data where the flow of time or context is important.

An RNN processes input not all at once, but in sequence, one at a time.

Simultaneously, it stores the information received up to that point internally and uses that information when processing the next input.

For instance, let's assume RNN processes the sentence "I am going to school."

When interpreting the phrase 'to school,' remembering the previous word 'I am' helps in understanding the sentence more accurately.

An RNN is designed with a structure that can reflect this flow.

Copy
Input: I am → going → to → school

RNN processes 'going' while remembering 'I am'
and processes 'to' while remembering 'I am, going.'

While RNN has the advantage of considering order, it has limitations in remembering old information well.

If a sentence is too long or the gap between inputs is too great, the RNN may forget earlier information and make less accurate predictions.

To address these issues, improved structures such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) have been developed.

These architectures are designed to retain long-term dependencies more effectively.

In the next lesson, we will delve into the structure of RNN, exploring the principles by which it remembers sequences and processes information.