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Duration 14 hours
Course Outline
Introduction to Pre-trained Models
- Defining pre-trained models
- Advantages of leveraging pre-trained solutions
- Review of prominent models (e.g., BERT, ResNet)
Delving into Model Architectures
- Foundations of model architecture
- Core concepts of transfer learning and fine-tuning
- Processes behind building and training pre-trained models
Environment Configuration
- Setting up Python and essential libraries
- Navigating model repositories (e.g., Hugging Face)
- Loading and evaluating pre-trained models
Practical Application
- Utilizing models for text classification
- Applying models to image recognition
- Fine-tuning models for bespoke datasets
Deployment Strategies
- Exporting and storing optimized models
- Integrating models into software applications
- Fundamentals of production deployment
Challenges and Best Practices
- Recognizing model limitations
- Mitigating overfitting during fine-tuning
- Promoting ethical AI usage
Emerging Trends
- Novel architectures and their utility
- Progress in transfer learning
- Investigation of large language and multimodal models
Conclusion and Path Forward
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in Python programming
- Basic experience with data manipulation using libraries such as Pandas
Intended Audience
- Data scientists
- AI professionals and enthusiasts
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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