Course Outline

Introduction to Generative AI and Large Language Models

  • Overview of generative AI and its evolution
  • Introduction to LLMs: GPT, BERT, and their capabilities
  • Comparing generative models with traditional NLP approaches

Transformer Architecture and Model Training

  • Understanding the transformer architecture in LLMs
  • Self-attention mechanism and language modeling
  • Training large language models and fine-tuning processes

Prompt Engineering for Effective Interaction

  • Crafting prompts for accurate and useful outputs
  • Fine-tuning prompt strategies for varied applications
  • Experimenting with prompt variations to optimize responses

Applications of LLMs in Business

  • Automating customer service with conversational AI
  • Content generation for marketing and media
  • LLMs in data analysis and report generation

Ethical Considerations and Bias Management

  • Identifying potential biases in LLM-generated content
  • Addressing ethical concerns in generative AI applications
  • Strategies for responsible deployment of LLMs

Advanced Techniques in LLMs

  • Fine-tuning LLMs for domain-specific applications
  • Integrating LLMs with other AI systems for enhanced functionality
  • Exploring multilingual and cross-lingual capabilities

The Future of Generative AI in Business

  • Emerging trends in generative AI and LLM research
  • Opportunities and challenges in scaling LLM solutions
  • Preparing for AI-driven transformation in business

Summary and Next Steps

Requirements

  • Basic understanding of machine learning and natural language processing concepts
  • Familiarity with Python programming

Audience

  • Data scientists and AI practitioners interested in generative AI technologies
  • Business professionals exploring automation and content generation
  • Technical managers and decision-makers looking to implement LLMs in their workflows
 14 Hours

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