Introduction to Pre-trained Models Training Course
Pre-trained models are a cornerstone of modern AI, offering pre-built capabilities that can be adapted for a variety of applications. This course introduces participants to the fundamentals of pre-trained models, their architecture, and their practical use cases. Participants will learn how to leverage these models for tasks such as text classification, image recognition, and more.
This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.
By the end of this training, participants will be able to:
- Understand the concept and benefits of pre-trained models.
- Explore various pre-trained model architectures and their use cases.
- Fine-tune a pre-trained model for specific tasks.
- Implement pre-trained models in simple machine learning projects.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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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
- Model architecture basics
- Transfer learning and fine-tuning concepts
- How pre-trained models are built 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
- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Basic knowledge of data handling using libraries like Pandas
Audience
- Data scientists
- AI enthusiasts
14 Hours
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