Sequence models are an essential component in the field of natural language processing (NLP). They are designed to handle sequences of data, such as text, audio, or video, and have been widely used in tasks like language translation, speech recognition, and text generation.

Key Concepts

  • RNN (Recurrent Neural Network): A type of neural network that is designed to work with sequences. It has loops within its architecture, allowing information to persist.
  • LSTM (Long Short-Term Memory): A type of RNN architecture that is capable of learning long-term dependencies. It is particularly useful for language tasks.
  • GRU (Gated Recurrent Unit): Another type of RNN architecture that is similar to LSTM but has a simpler structure.

Applications

  • Language Translation: Sequence models are used to translate text from one language to another.
  • Speech Recognition: They can convert spoken words into written text.
  • Text Generation: Sequence models can be used to generate text, such as articles or stories.

Language Translation Example

Resources

For further reading, you can explore the following resources:

Note: The images used in this document are for illustrative purposes only.