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Course Outline
Introduction to Generative AI
- What is generative AI and why is it important?
- Main types and techniques of generative AI
- Key challenges and limitations of generative AI
Transformer Architecture and LLMs
- What is a transformer and how does it work?
- Main components and features of a transformer
- Using transformers to build LLMs
Scaling Laws and Optimization
- What are scaling laws and why are they important for LLMs?
- How do scaling laws relate to the model size, data size, compute budget, and inference requirements?
- How can scaling laws help optimize the performance and efficiency of LLMs?
Training and Fine-Tuning LLMs
- Main steps and challenges of training LLMs from scratch
- Benefits and drawbacks of fine-tuning LLMs for specific tasks
- Best practices and tools for training and fine-tuning LLMs
Deploying and Using LLMs
- Main considerations and challenges of deploying LLMs in production
- Common use cases and applications of LLMs in various domains and industries
- Integrating LLMs with other AI systems and platforms
Ethics and Future of Generative AI
- Ethical and social implications of generative AI and LLMs
- Potential risks and harms of generative AI and LLMs, such as bias, misinformation, and manipulation
- Responsible and beneficial use of generative AI and LLMs
Summary and Next Steps
Requirements
- An understanding of machine learning concepts, such as supervised and unsupervised learning, loss functions, and data splitting
- Experience with Python programming and data manipulation
- Basic knowledge of neural networks and natural language processing
Audience
- Developers
- Machine learning enthusiasts
21 Hours
