RNN in Classic Algorithms: A Must-Know for Developers

RNNs handle sequence data like text and speech with memory capabilities, but face vanishing and exploding gradient challenges. LSTMs solve these, aiding tasks like text generation.

Welcome to the "Practical Application of AI Large Language Model Systems" Series

Table of Contents

In the last class, we introduced neural networks. There are many types, including Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and Transformer models.

Today, we will focus on RNNs, which are mainly used for sequence data. Why should we learn about RNNs?

Most large language models today are based on Transformers. By learning RNNs, we can understand how neural networks handle dependencies in sequences, remember past information, and generate predictions. This knowledge helps us grasp key issues like vanishing and exploding gradients, laying a foundation for understanding Transformers.