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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
