Course Program

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#1Introduction to Large Language Models
  • What Are Language Models?---
  • History of Language Models---
  • Applications of Language Models---
#2The Mechanics of Language Models
  • Tokenization and Embeddings---
  • Training Neural Language Models---
  • Attention Mechanism---
  • Transformers Architecture---
#3Training Data and Techniques
  • Data Collection Methods---
  • Data Preprocessing Techniques---
  • Supervised vs Unsupervised Learning---
  • Scalability in Training---
#4Advanced Model Architectures
  • GPT Series---
  • BERT and Its Variants---
  • ELMO---
  • T5: Text-to-Text Framework---
#5Ethical Concerns
  • Bias in Language Models---
  • Fairness in AI---
  • Ensuring Robustness---
  • GPT-3 Ethical Considerations---
  • GPT-4 Ethical Considerations---
  • GPT-5 Ethical Considerations---
#6Methods for Model Evaluation
  • Evaluation Metrics---
  • Baseline Comparison---
  • Real-World Performance Testing---
#7Improving Model Performance
  • Transfer Learning and Fine-Tuning---
  • Hyperparameter Tuning---
  • Error Analysis---
  • Using Ensemble Methods---
#8Applications in Diverse Fields
  • Healthcare---
  • Finance---
  • Legal Frameworks---
  • Game Development---
#9Implementing Language Models
  • Deploying Models---
  • Scaling APIs---
  • Integrating with Other Systems---
#10Emerging Trends
  • AI and Personalization---
  • Future Research Directions---
  • Interactive and Conversational AI---