Course Outline

Introduction to Generative Pre-trained Transformers (GPT)

  • Evolution of language models in NLP
  • Introduction to GPT and its significance
  • Use cases and applications of GPT models

Understanding GPT Architecture and Training

  • Transformer architecture and self-attention mechanism
  • Pre-training and fine-tuning of GPT models
  • Transfer learning and domain adaptation with GPT

Exploring GPT-3

  • Overview of GPT-3 architecture and features
  • Understanding the model's capabilities and limitations
  • Hands-on exercises with GPT-3 for text generation and completion

Recent Advancements: GPT-4

  • Overview of the latest GPT-4 model
  • Key enhancements and improvements over previous versions
  • Exploring the expanded capabilities of GPT-4

Applications of GPT Models

  • Text generation and completion using GPT models
  • Machine translation with GPT
  • Dialogue systems and chatbots with GPT
  • Creative writing and storytelling using GPT models

Fine-tuning GPT Models

  • Techniques for fine-tuning GPT models on specific tasks
  • Adapting GPT for domain-specific applications
  • Best practices for fine-tuning and model evaluation

Ethical Considerations and Challenges

  • Ethical implications of using large language models
  • Bias and fairness issues in GPT models
  • Mitigating risks and ensuring responsible use of GPT models

Future Trends and Beyond GPT-4

  • Emerging trends in NLP and generative models
  • Research frontiers and potential advancements beyond GPT-4

Summary and Next Steps

  • Recap of key learnings and takeaways from the course
  • Resources for further exploration and learning opportunities in GPT models and NLP

Requirements

  • Familiarity with deep learning concepts and natural language processing (NLP) fundamentals. 
  • Basic knowledge of transformers would be beneficial.

Audience

  • Data scientists
  • Machine learning engineers
  • NLP researchers
  • AI enthusiasts
 14 Hours

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