🤖 A foundational guide to understanding the Transformer architecture and its applications in NLP

What is Transformer?

The Transformer is a revolutionary neural network architecture introduced in this paper. It replaces traditional RNNs with self-attention mechanisms, enabling parallel processing and better handling of long-range dependencies.

Key Components

  • Self-Attention:

    self_attention
    *Visualizing how tokens interact with each other in a sequence*

  • Multi-Head Attention:

    multi_head_attention
    *Highlighting the parallel processing of multiple attention heads*

  • Positional Encoding:

    positional_encoding
    *Adding position-specific information to token embeddings*

Applications

  • Machine Translation
  • Text Summarization
  • Question Answering
  • Chatbots and Dialogue Systems

Further Reading

For a deeper dive into the attention mechanism, check out our tutorial:
Attention Mechanism Explained

Note: This content is purely educational and complies with all regulations.