Course Outline
Introduction to Pre-trained Models
- Defining pre-trained models
- Advantages of utilizing pre-trained models
- A look at popular pre-trained models (e.g., BERT, ResNet)
Delving into Pre-trained Model Architectures
- Foundations of model architecture
- Concepts of transfer learning and fine-tuning
- The process of building and training pre-trained models
Environment Configuration
- Installation and setup of Python and essential libraries
- Navigating pre-trained model repositories (e.g., Hugging Face)
- Loading and testing pre-trained models
Practical Application of Pre-trained Models
- Leveraging pre-trained models for text classification
- Using pre-trained models for image recognition
- Fine-tuning models for custom datasets
Deployment of Pre-trained Models
- Exporting and storing fine-tuned models
- Integrating models into software applications
- Foundations of deploying models in production environments
Challenges and Best Practices
- Recognizing model limitations
- Preventing overfitting during the fine-tuning process
- Promoting ethical AI model usage
Emerging Trends in Pre-trained Models
- New architectures and their potential applications
- Progress in transfer learning
- Exploration of large language models and multimodal models
Overview and Future Directions
Requirements
- A foundational grasp of machine learning principles
- Proficiency in Python programming
- Basic competence in data management using libraries such as Pandas
Target Audience
- Data scientists
- AI enthusiasts
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
