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Course Outline
Introduction to Fine-Tuning
- What is fine-tuning?
- Use cases and benefits of fine-tuning
- Overview of pre-trained models and transfer learning
Preparing for Fine-Tuning
- Collecting and cleaning datasets
- Understanding task-specific data requirements
- Exploratory data analysis and preprocessing
Fine-Tuning Techniques
- Transfer learning and feature extraction
- Fine-tuning transformers with Hugging Face
- Fine-tuning for supervised vs unsupervised tasks
Fine-Tuning Large Language Models (LLMs)
- Adapting LLMs for NLP tasks (e.g., text classification, summarization)
- Training LLMs with custom datasets
- Controlling LLM behavior with prompt engineering
Optimization and Evaluation
- Hyperparameter tuning
- Evaluating model performance
- Addressing overfitting and underfitting
Scaling Fine-Tuning Efforts
- Fine-tuning on distributed systems
- Leveraging cloud-based solutions for scalability
- Case studies: Large-scale fine-tuning projects
Best Practices and Challenges
- Best practices for fine-tuning success
- Common challenges and troubleshooting
- Ethical considerations in fine-tuning AI models
Advanced Topics (Optional)
- Fine-tuning multi-modal models
- Zero-shot and few-shot learning
- Exploring LoRA (Low-Rank Adaptation) techniques
Summary and Next Steps
Requirements
- Understanding of machine learning fundamentals
- Experience with Python programming
- Familiarity with pre-trained models and their applications
Audience
- Data scientists
- Machine learning engineers
- AI researchers
14 Hours
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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