Deep Dive into Sequences: Unveiling Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
In our previous articles, we explored the initial steps of Natural Language Processing (NLP), from tokenization to creating sequences. With the foundations set, let’s shift gears and move towards a more intriguing component of NLP: handling sequential data with Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM). This article aims to demystify these fascinating techniques, allowing you to understand and implement them effectively in your NLP tasks.
Why Sequences Matter in NLP
When dealing with language, sequence matters. “The cat ate the mouse” has a drastically different meaning from “The mouse ate the cat”. Traditional feed-forward networks lack the ability to maintain the order of words, leading to poor performance on sequential tasks. This is where RNNs and LSTMs come into play.
Understanding Recurrent Neural Networks (RNNs)
The Concept of RNNs
Recurrent Neural Networks, as the name suggests, have loops in them, providing a form of internal memory that helps them remember past inputs in the sequence. This characteristic makes them ideal for processing sequential data.
