Transformer Model Architectures
Transformer models are a type of deep learning architecture that has revolutionized Natural Language Processing (NLP) and generative AI. Introduced in 2017, they efficiently process sequential data and excel in understanding context.
Core Concepts
- Attention Mechanism: Allows the model to focus on relevant parts of input sequences.
- Encoder-Decoder Architecture: Encoders process input data, and decoders generate output.
- Self-Attention: Enables the model to understand relationships between words in a sequence.
- Scalability: Transformers can be scaled with more layers and parameters for higher performance.
Popular Transformer Models
- BERT (Bidirectional Encoder Representations from Transformers) – excels at understanding context in text.
- GPT (Generative Pre-trained Transformer) – specializes in text generation and conversational AI.
- T5 (Text-to-Text Transfer Transformer) – flexible NLP tasks from translation to summarization.
- Vision Transformers (ViT) – applies transformer architecture to image recognition tasks.
Applications
- Chatbots and conversational AI
- Text summarization and translation
- Sentiment analysis and recommendation systems
- Image classification and generation with Vision Transformers
Learn More
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- Deep Learning and Neural Networks
- Generative AI: Text, Image, Video
- Prompt Engineering & Fine-tuning
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