RNN vs. LSTM vs. Transformers: Unraveling the Secrets of Sequential Data Processing

In the realm of deep learning, sequential data processing is at the heart of many applications, including natural language understanding, speech recognition, and time series forecasting. Three prominent architectures — Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers — have emerged as pivotal tools for handling sequential data. In this comprehensive guide, we dive deep into these models, dissecting their capabilities, limitations, and real-world applications.

The Foundation: Recurrent Neural Networks (RNNs)

RNNs, with their recurrent connections, laid the foundation for sequence modeling. They process data sequentially, maintaining a hidden state that captures information from previous time steps. This enables them to handle sequential dependencies naturally.

Advantages of RNNs

• Sequential Dependencies: RNNs are well-suited for tasks where past information significantly impacts future predictions, such as language modeling and time series forecasting.
• Simple and Intuitive: The simplicity of RNNs makes them easy to understand and implement.

Limitations of RNNs

• Vanishing and Exploding Gradients: RNNs often suffer from vanishing or exploding gradients, making it challenging to capture long-range dependencies.