🧠 Transformer Model Overview
Transformer is a groundbreaking architecture in deep learning, introduced in 2017 by Google's research team. It revolutionizes sequence modeling by relying entirely on self-attention mechanisms instead of traditional recurrence.

Key Features

  • Self-Attention: Enables parallel processing of input sequences, capturing dependencies across positions.
  • Positional Encoding: Adds positional information to token embeddings for sequential context.
  • Scalability: Handles long-range dependencies more effectively than RNNs or CNNs.

Applications

  • Natural Language Processing (NLP):
    • Machine translation (e.g., Google's BERT)
    • Text summarization
    • Question answering
  • Computer Vision:
    • Vision Transformers (ViT) for image classification
    • Object detection with attention-based models

Learning Resources

  1. Official Paper: Attention Is All You Need
  2. Interactive Demo: Visualizing Transformers
  3. GitHub Repository: Open-source implementations
Transformer Architecture
For deeper insights, explore our [Attention Mechanism Guide](/en/resources/deep-learning/attention-mechanism) to understand its foundational principles.

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