Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course
Large Language Models (LLMs) represent advanced neural network architectures designed to comprehend and produce human-like text in response to input. Reinforcement Learning (RL) constitutes a machine learning paradigm where an agent acquires decision-making capabilities by executing actions within an environment to maximize cumulative rewards.
This instructor-led live training, available either online or onsite, targets intermediate-level data scientists seeking a thorough understanding and practical proficiency in both Large Language Models (LLMs) and Reinforcement Learning (RL).
Upon completion of this training, participants will be equipped to:
- Comprehend the components and operational mechanics of transformer models.
- Optimize and fine-tune LLMs for specific tasks and applications.
- Grasp the core principles and methodologies underlying reinforcement learning.
- Explore how reinforcement learning techniques can bolster the performance of LLMs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- 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 (LLMs)
- Overview of LLMs
- Definition and significance
- Applications in AI today
Transformer Architecture
- Understanding what a transformer is and its operational mechanism
- Key components and features
- Embedding and positional encoding
- Multi-head attention
- Feed-forward neural network
- Normalization and residual connections
Transformer Models
- Self-attention mechanism
- Encoder-decoder architecture
- Positional embeddings
- BERT (Bidirectional Encoder Representations from Transformers)
- GPT (Generative Pretrained Transformer)
Performance Optimization and Pitfalls
- Context length
- Mamba and state-space models
- Flash attention
- Sparse transformers
- Vision transformers
- The importance of quantization
Enhancing Transformers
- Retrieval augmented text generation
- Mixture of models
- Tree of thoughts
Fine-Tuning
- Theory of low-rank adaptation
- Fine-Tuning with QLora
Scaling Laws and Optimization in LLMs
- Significance of scaling laws for LLMs
- Scaling of data and model size
- Computational scaling
- Parameter efficiency scaling
Optimization
- Interplay between model size, data volume, compute budget, and inference requirements
- Strategies for optimizing LLM performance and efficiency
- Best practices and tools for training and fine-tuning LLMs
Training and Fine-Tuning LLMs
- Procedural steps and challenges of training LLMs from scratch
- Data acquisition and maintenance
- Requirements for large-scale data, CPU, and memory
- Optimization challenges
- Overview of open-source LLM landscape
Fundamentals of Reinforcement Learning (RL)
- Introduction to Reinforcement Learning
- Learning through positive reinforcement
- Definition and core 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
Integration of LLMs and Reinforcement Learning
- Combining LLMs with Reinforcement Learning
- Application of RL in LLMs
- Reinforcement Learning with Human Feedback (RLHF)
- Alternatives to RLHF
Case Studies and Applications
- Real-world applications
- Success stories and challenges
Advanced Topics
- Advanced techniques
- Advanced optimization methods
- Cutting-edge research and developments
Summary and Next Steps
Requirements
- Foundational knowledge of Machine Learning
Target Audience
- Data scientists
- Software engineers
Open Training Courses require 5+ participants.
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