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Computer Science
Artificial Intelligence
Generative AI
1. Introduction to Generative AI
2. Mathematical and Technical Foundations
3. Machine Learning Fundamentals
4. Core Generative Model Architectures
5. Transformer Architecture and Language Models
6. Text Generation Applications
7. Image and Visual Generation
8. Audio and Speech Generation
9. Development Lifecycle and Best Practices
10. Practical Implementation Tools
11. Ethical Considerations and Responsible AI
12. Legal and Regulatory Landscape
13. Economic and Social Impact
14. Future Directions and Emerging Trends
Development Lifecycle and Best Practices
Project Planning and Design
Problem Definition
Use Case Identification
Success Metrics Definition
Feasibility Assessment
Architecture Design
Model Selection
Infrastructure Planning
Scalability Considerations
Data Management
Data Collection Strategies
Public Dataset Utilization
Proprietary Data Gathering
Synthetic Data Generation
Data Preprocessing
Cleaning and Filtering
Normalization Techniques
Augmentation Strategies
Data Quality Assurance
Bias Detection
Quality Metrics
Validation Procedures
Model Development
Training Strategies
Pretraining Approaches
Transfer Learning
Fine-Tuning Techniques
Hyperparameter Optimization
Grid Search
Random Search
Bayesian Optimization
Regularization and Validation
Cross-Validation
Early Stopping
Model Ensembling
Evaluation and Testing
Quantitative Metrics
Perplexity for Language Models
FID for Image Generation
BLEU for Translation
Qualitative Assessment
Human Evaluation
Expert Review
User Studies
Robustness Testing
Adversarial Examples
Out-of-Distribution Testing
Stress Testing
Deployment and Operations
Model Serving
API Development
Load Balancing
Caching Strategies
Monitoring and Maintenance
Performance Monitoring
Model Drift Detection
Automated Retraining
Version Control
Model Versioning
Experiment Tracking
Rollback Procedures
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8. Audio and Speech Generation
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10. Practical Implementation Tools