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Course Outline
Introduction to Large Language Models (LLMs)
- Overview of LLMs for government
- Definition and significance in public sector applications
- Current applications in artificial intelligence for government workflows
Transformer Architecture
- Description and operational principles of transformers
- Key components and features relevant to government use cases
- Embedding and positional encoding techniques
- Multi-head attention mechanisms for enhanced data processing
- Feed-forward neural networks in transformer models
- Normalization and residual connections for improved model performance
Transformer Models
- Self-attention mechanism in government applications
- Encoder-decoder architecture for robust data handling
- Positional embeddings for context-aware models
- BERT (Bidirectional Encoder Representations from Transformers) and its relevance to public sector tasks
- GPT (Generative Pretrained Transformer) and its applications in government
Performance Optimization and Pitfalls
- Context length considerations for efficient model deployment
- Mamba and state-space models for enhanced performance
- Flash attention techniques for faster processing
- Sparse transformers for resource optimization
- Vision transformers for visual data analysis in government applications
- The importance of quantization for model efficiency
Improving Transformers
- Retrieval augmented text generation for enhanced accuracy
- Mixture of models for versatile problem-solving
- Tree of thoughts for complex decision-making processes
Fine-Tuning
- Theory of low-rank adaptation for efficient model customization
- Fine-tuning with QLora for government-specific tasks
Scaling Laws and Optimization in LLMs
- Significance of scaling laws for large language models in government applications
- Data and model size scaling considerations
- Computational scaling to meet performance requirements
- Parameter efficiency scaling for resource-constrained environments
Optimization
- Interplay between model size, data size, compute budget, and inference needs in government contexts
- Strategies for optimizing performance and efficiency of LLMs for government use
- Best practices and tools for training and fine-tuning LLMs in the public sector
Training and Fine-Tuning LLMs
- Steps and challenges involved in training LLMs from scratch for government purposes
- Data acquisition and maintenance strategies for government datasets
- Large-scale data, CPU, and memory requirements for robust model training
- Optimization challenges specific to public sector applications
- Landscape of open-source LLMs suitable for government use
Fundamentals of Reinforcement Learning (RL)
- Introduction to reinforcement learning for government applications
- Learning through positive reinforcement in public sector contexts
- Definition and core concepts of RL for government tasks
- Markov Decision Process (MDP) in government decision-making
- Dynamic programming techniques for efficient problem-solving
- Monte Carlo methods for probabilistic modeling in government
- Temporal Difference Learning for incremental learning processes
Deep Reinforcement Learning
- Deep Q-Networks (DQN) and their applications in government
- Proximal Policy Optimization (PPO) for robust policy development
- Key elements of reinforcement learning relevant to public sector tasks
Integration of LLMs and Reinforcement Learning
- Combining large language models with reinforcement learning for government applications
- Utilizing RL in the context of LLMs for enhanced decision-making
- Reinforcement Learning with Human Feedback (RLHF) for improved model performance
- Alternatives to RLHF for diverse public sector needs
Case Studies and Applications
- Real-world applications of LLMs and reinforcement learning in government
- Success stories and challenges faced in implementing these technologies
Advanced Topics
- Advanced techniques for optimizing and deploying models in the public sector
- Cutting-edge research and developments relevant to government applications
Summary and Next Steps
Requirements
- Foundational knowledge of Machine Learning for government
Audience
- Data scientists
- Software engineers
21 Hours
