Hello again! In today's lesson, we'll delve into the fascinating world of Recurrent Neural Networks (RNNs) and explore their application in text classification. Whether you are new to this concept or have some familiarity with it from your Natural Language Processing (NLP) journey, you'll appreciate the unique capabilities of RNNs in handling sequential data, such as text or time series.

RNNs are distinctive because they have a form of memory. They retain the output of a layer and feed it back into the input to assist in predicting the layer's outcome. To understand this better, think of how we read a novel: we don't start from scratch on each new page but build our comprehension based on all the previous pages. Similarly, RNNs remember everything they've processed up to a given point, using this information to generate current output.

Due to their ability to capture temporal dependencies in sequences, RNNs excel in NLP tasks. They leverage past information to understand context more effectively, making them ideal for language modeling, translation, sentiment analysis, and our focus for today — text classification.

Before we proceed, it's crucial to recall the pre-processing steps performed on our data: