Sequence models are fundamental in natural language processing (NLP) for tasks like text generation, machine translation, and speech recognition. Here's a quick overview of key architectures:

  • Recurrent Neural Networks (RNNs) 🧠
    Classic models with loops allowing information persistence.

    Recurrent Neural Network

  • Long Short-Term Memory (LSTM) 📈
    Improved RNN with memory cells to capture long-range dependencies.

    Long Short-Term Memory

  • Gated Recurrent Units (GRUs) ⏱️
    Simplified version of LSTMs with fewer parameters.

    Gated Recurrent Units

  • Transformer Models 🔄
    Attention-based architecture revolutionizing sequence processing.

    Transformer Model

For deeper insights into Transformer mechanisms, visit our Transformer Tutorial.