Course Outline
Introduction to Pre-trained Models
- What are pre-trained models?
- Benefits of using pre-trained models.
- Overview of popular pre-trained models (e.g., BERT, ResNet).
Understanding Pre-trained Model Architectures
- Basics of model architecture.
- Concepts of transfer learning and fine-tuning.
- How pre-trained models are constructed and trained.
Setting Up the Environment
- Installing and configuring Python and relevant libraries.
- Exploring pre-trained model repositories (e.g., Hugging Face).
- Loading and testing pre-trained models.
Hands-On with Pre-trained Models
- Using pre-trained models for text classification.
- Applying pre-trained models to image recognition tasks.
- Fine-tuning pre-trained models for custom datasets.
Deploying Pre-trained Models
- Exporting and saving fine-tuned models.
- Integrating models into applications.
- Basics of deploying models in production.
Challenges and Best Practices
- Understanding model limitations.
- Avoiding overfitting during fine-tuning.
- Ensuring ethical use of AI models.
Future Trends in Pre-trained Models
- Emerging architectures and their applications.
- Advances in transfer learning.
- Exploring large language models and multimodal models.
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts.
- Familiarity with Python programming.
- Basic proficiency in data handling using libraries such as Pandas.
Target Audience
- Data scientists.
- AI enthusiasts.
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 3200 € + VAT*
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Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
