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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
- Fundamental 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
Testimonials (1)
Trainer was very knowledgeable and easy to speak to
