Course Outline
Introduction to Pre-trained Models
- Defining pre-trained models
- Key advantages of utilizing pre-trained models
- Overview of widely adopted models (e.g., BERT, ResNet)
Exploring Pre-trained Model Architectures
- Foundations of model architecture
- Concepts of transfer learning and fine-tuning
- The process of building and training pre-trained models
Configuring the Environment
- Installation and setup of Python and essential libraries
- Navigating pre-trained model repositories (e.g., Hugging Face)
- Loading and evaluating pre-trained models
Practical Application of Pre-trained Models
- Utilizing pre-trained models for text classification
- Applying pre-trained models to image recognition tasks
- Fine-tuning models for custom datasets
Deployment of Pre-trained Models
- Saving and exporting fine-tuned models
- Integrating models into software applications
- Fundamentals of production deployment
Challenges and Best Practices
- Recognizing model limitations
- Mitigating overfitting during the fine-tuning process
- Ensuring ethical application of AI models
Emerging Trends in Pre-trained Models
- New architectural developments and their uses
- Progress in transfer learning techniques
- Exploration of large language and multimodal models
Conclusion and Next Steps
Requirements
- A foundational grasp of machine learning principles
- Proficiency in Python programming
- Basic proficiency in data management using libraries such as Pandas
Target Audience
- Data scientists
- Professionals interested in AI
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
