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Duration 14 hours
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 (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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