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

Delivery Options

Private Group Training

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