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Course Outline
Introduction to Reinforcement Learning from Human Feedback (RLHF)
- Understanding RLHF and its significance.
- Comparing RLHF with supervised fine-tuning methods.
- Applications of RLHF in modern AI systems.
Reward Modeling with Human Feedback
- Collecting and structuring human feedback.
- Building and training reward models.
- Evaluating the effectiveness of reward models.
Training with Proximal Policy Optimization (PPO)
- Overview of PPO algorithms for RLHF.
- Implementing PPO alongside reward models.
- Iteratively and safely fine-tuning models.
Practical Fine-Tuning of Language Models
- Preparing datasets for RLHF workflows.
- Hands-on fine-tuning of a small LLM using RLHF.
- Challenges and mitigation strategies.
Scaling RLHF to Production Systems
- Infrastructure and compute considerations.
- Quality assurance and continuous feedback loops.
- Best practices for deployment and maintenance.
Ethical Considerations and Bias Mitigation
- Addressing ethical risks in human feedback.
- Strategies for bias detection and correction.
- Ensuring alignment and safe outputs.
Case Studies and Real-World Examples
- Case study: Fine-tuning ChatGPT with RLHF.
- Other successful RLHF deployments.
- Lessons learned and industry insights.
Summary and Next Steps
Requirements
- A solid understanding of supervised and reinforcement learning fundamentals.
- Practical experience in model fine-tuning and neural network architectures.
- Familiarity with Python programming and deep learning frameworks (e.g., TensorFlow, PyTorch).
Target Audience
- Machine learning engineers.
- AI researchers.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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