Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course
Large Language Models (LLMs) are sophisticated neural network architectures designed to comprehend and produce human-like text based on provided inputs. Reinforcement Learning (RL) represents a machine learning approach where an agent acquires decision-making capabilities by executing actions within an environment to maximize cumulative rewards.
This instructor-led, live training, available both online and on-site, targets intermediate-level data scientists seeking a thorough understanding and practical expertise in both Large Language Models (LLMs) and Reinforcement Learning (RL).
Upon completion of this training, participants will be equipped to:
- Grasp the components and operational mechanics of transformer models.
- Optimize and fine-tune LLMs for specific tasks and applications.
- Comprehend the fundamental principles and methodologies of reinforcement learning.
- Explore how reinforcement learning techniques can improve the performance of LLMs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request tailored training for this course, please reach out to us to arrange.
Course Outline
Introduction to Large Language Models (LLMs)
- Overview of LLMs
- Definition and significance
- Applications in AI today
Transformer Architecture
- What is a transformer and how does it work?
- Main 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
- Importance of quantization
Improving 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
- Importance of scaling laws for LLMs
- Data and model size scaling
- Computational scaling
- Parameter efficiency scaling
Optimization
- Relationship between model size, data size, compute budget, and inference requirements
- Optimizing performance and efficiency of LLMs
- Best practices and tools for training and fine-tuning LLMs
Training and Fine-Tuning LLMs
- Steps and challenges of training LLMs from scratch
- Data acquisition and maintenance
- Large-scale data, CPU, and memory requirements
- Optimization challenges
- Landscape of open-source LLMs
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)
- Elements of Reinforcement Learning
Integration of LLMs and Reinforcement Learning
- Combining LLMs with Reinforcement Learning
- How RL is used 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
Audience
- Data scientists
- Software engineers
Open Training Courses require 5+ participants.
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