Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course
Large Language Models (LLMs) constitute advanced neural network architectures engineered to comprehend and produce text resembling human communication in response to input data. Reinforcement Learning (RL) represents a machine learning paradigm wherein an autonomous agent acquires decision-making capabilities by executing actions within an environment, with the objective of maximizing cumulative rewards.
This instructor-led, live training session—available via online or onsite delivery—is designed for intermediate-level data scientists seeking a thorough grasp and practical proficiency in both Large Language Models (LLMs) and Reinforcement Learning (RL). This curriculum is tailored for government professionals to ensure alignment with public sector requirements for government
Upon completion of this training, participants will be equipped to:
- Analyze the architecture and operational mechanics of transformer models.
- Optimize and fine-tune LLMs to address specific tasks and applications.
- Comprehend the fundamental principles and methodologies underlying reinforcement learning.
- Evaluate how reinforcement learning techniques can improve the performance of LLMs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- For inquiries regarding customized training for this course, please contact us to arrange details.
Course Outline
Introduction to Large Language Models
- Strategic overview of large language models
- Core definitions and operational significance
- Current applications within artificial intelligence frameworks
Transformer Architecture Fundamentals
- Principles and functionality of transformer systems
- Key architectural components and capabilities
- Implementation of embeddings and positional encoding
- Mechanics of multi-head attention
- Structure of feed-forward neural networks
- Role of normalization and residual connections
Evaluating Transformer Models
- Functionality of the self-attention mechanism
- Encoder-decoder structural design
- Utilization of positional embeddings
- BERT (Bidirectional Encoder Representations from Transformers)
- GPT (Generative Pretrained Transformer)
Performance Optimization and Limitations
- Management of context length
- Mamba models and state-space architectures
- Efficiencies of Flash attention
- Applications of sparse transformers
- Vision transformer implementations
- Strategic importance of quantization for government systems
Enhancing Transformer Capabilities
- Retrieval-augmented text generation techniques
- Mixture of experts and model integration
- Tree of thoughts reasoning frameworks
Fine-Tuning Methodologies
- Theoretical foundations of low-rank adaptation
- Implementation of QLora for fine-tuning
Scaling Laws and Optimization in Large Language Models
- Significance of scaling laws in model development
- Scaling parameters through data and model size
- Computational resource scaling
- Optimization of parameter efficiency
Optimization Strategies
- Interdependencies among model scale, data volume, compute budget, and inference needs
- Strategies for optimizing the performance and efficiency of large language models
- Established best practices and tools for training and fine-tuning for government use cases
Training and Fine-Tuning Large Language Models
- Procedural steps and challenges in developing foundational models
- Data acquisition strategies and maintenance protocols
- Requirements for large-scale data, CPU resources, and memory capacity
- Challenges in optimization processes
- Overview of the open-source large language model ecosystem
Fundamentals of Reinforcement Learning
- Introduction to reinforcement learning principles
- Learning mechanisms via positive reinforcement
- Core definitions and conceptual frameworks
- Markov Decision Process (MDP) applications
- Dynamic programming methodologies
- Monte Carlo methods
- Temporal Difference Learning techniques
Deep Reinforcement Learning
- Deep Q-Networks (DQN)
- Proximal Policy Optimization (PPO)
- Key elements of reinforcement learning systems
Integration of Large Language Models and Reinforcement Learning
- Synergies between large language models and reinforcement learning
- Operational use of reinforcement learning in model alignment
- Reinforcement Learning from Human Feedback (RLHF)
- Alternatives to RLHF for government applications
Case Studies and Applications
- Practical real-world implementations
- Documented success metrics and operational challenges
Advanced Topics
- High-level technical techniques
- Advanced optimization methodologies
- Emerging research trends and developments
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning principles
Target Audience
- Data scientists
- Software engineers
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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