NLP & Transformers

BERT, HuggingFace, fine-tuning LLMs

Author: AI Learning Club Team · Difficulty: Intermediate · Duration: 2 hr 15 min · Modules: 26

Modules

1. Introduction to NLP (6 min)
2. Overview of Transformers (6 min)
3. Understanding BERT (8 min)
4. Introduction to HuggingFace (6 min)
5. Setting Up the Environment (8 min)
6. Basics of Tokenization (8 min)
7. Training a Simple Transformer Model (8 min)
8. Fine-tuning BERT for a Specific Task (8 min)
9. Advanced Tokenization Techniques (8 min)
10. Optimizing Transformer Models (8 min)
11. Handling Large Datasets (8 min)
12. Deploying Transformer Models (8 min)
13. Evaluating Model Performance (8 min)
14. Common Challenges in NLP (8 min)
15. Ethics in NLP (8 min)
16. Case Studies in NLP Applications (8 min)
17. Future Trends in NLP (8 min)
18. Building Custom NLP Pipelines (8 min)
19. Debugging and Troubleshooting (8 min)
20. Collaborative NLP Projects (12 min)
21. NLP in Industry Applications (8 min)
22. Advanced Fine-tuning Techniques (8 min)
23. Scaling Up Transformer Models (8 min)
24. Performance Optimization Strategies (8 min)
25. Capstone Project (12 min)
26. Resources & References (8 min)

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