Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course

Large Language Models (LLMs) are sophisticated neural network architectures designed to comprehend and generate human-like text in response to input prompts. Reinforcement Learning (RL) constitutes a branch of machine learning wherein an agent acquires decision-making capabilities by executing actions within an environment to maximize cumulative rewards.

This instructor-led, live training session, available in online or onsite formats in Nigeria, targets intermediate-level data scientists seeking a thorough grasp and practical proficiency in both Large Language Models (LLMs) and Reinforcement Learning (RL).

Upon completion of this training, participants will be able 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 of reinforcement learning.
Understand how reinforcement learning techniques can enhance the performance of LLMs.

Format of the Course

Interactive lecture and discussion.
Extensive exercises and practice sessions.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course is available as onsite live training in Nigeria or online live training.

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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

Basic understanding of Machine Learning

Audience

Data scientists
Software engineers

21 Hours

Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793

Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course

Course Outline

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Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course

Course Outline

Requirements

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