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Duration 21 hours
Course Outline
Introduction to Large Language Models (LLMs)
- Overview of LLMs
- Definitions and industry significance
- Current applications in Artificial Intelligence
Transformer Architecture
- Explaining the transformer mechanism
- Key components and characteristics
- Embedding and positional encoding
- Multi-head attention
- Feed-forward neural networks
- Normalization and residual connections
Transformer Models
- The self-attention mechanism
- Encoder-decoder structures
- Positional embeddings
- BERT (Bidirectional Encoder Representations from Transformers)
- GPT (Generative Pretrained Transformer)
Performance Optimization and Common Pitfalls
- Managing context length
- Mamba and state-space models
- Flash attention
- Sparse transformers
- Vision transformers
- The role of quantization
Enhancing Transformer Capabilities
- Retrieval-augmented text generation
- Mixture of models
- Tree of thoughts
Fine-Tuning
- Low-rank adaptation theory
- Fine-tuning utilizing QLora
Scaling Laws and Optimization in LLMs
- The impact of scaling laws on LLMs
- Scaling data and model sizes
- Computational scaling
- Parameter efficiency scaling
Optimization Strategies
- Correlations between model size, data volume, compute budgets, and inference needs
- Improving LLM performance and efficiency
- Best practices and tools for LLM training and fine-tuning
Training and Fine-Tuning LLMs
- Processes and challenges in training LLMs from scratch
- Data acquisition and upkeep
- Requirements for large-scale data, CPU, and memory
- Optimization hurdles
- Review of open-source LLMs
Fundamentals of Reinforcement Learning (RL)
- Introduction to Reinforcement Learning
- Learning via positive reinforcement
- Core definitions and concepts
- Markov Decision Process (MDP)
- Dynamic programming
- Monte Carlo methods
- Temporal Difference Learning
Deep Reinforcement Learning
- Deep Q-Networks (DQN)
- Proximal Policy Optimization (PPO)
- Core elements of Reinforcement Learning
Integrating LLMs and Reinforcement Learning
- Combining LLMs with RL frameworks
- The role of RL within LLMs
- Reinforcement Learning with Human Feedback (RLHF)
- Alternatives to RLHF
Case Studies and Applications
- Real-world use cases
- Success stories and associated challenges
Advanced Topics
- Advanced techniques
- Sophisticated optimization methods
- Latest research and technological developments
Summary and Future Directions
Requirements
- Familiarity with the fundamentals of Machine Learning
Target Audience
- Data scientists
- Software engineers
