Transformer is a groundbreaking architecture in natural language processing (NLP) that revolutionizes sequence modeling by leveraging self-attention mechanisms. Unlike traditional RNNs or CNNs, it processes input in parallel, making it highly efficient for tasks like machine translation and text generation. Here's a breakdown:

Key Components 🛠️

  • Self-Attention Mechanism
    ⚡ Enables the model to weigh the importance of different words in a sentence dynamically.

    Self_attention_mechanism

  • Positional Encoding
    🧩 Adds information about the position of tokens to the input embeddings.

    Transformer_architecture

  • Multi-Layer Perceptron (MLP)
    🔄 Used in feed-forward layers between attention blocks for non-linear transformations.

    Feed_forward_layer

Applications 🚀

  • Machine translation (e.g., Google Translate)
  • Text summarization
  • Question answering systems
  • Generative AI models like GPT and BERT

For deeper insights, explore our guide on Attention Mechanism or Deep Learning Tutorials.

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