Evaluating Transfer Learning Techniques to Enhance Machine Learning Models in Artificial Intelligence Applications

1. Introduction 2. Overview of Machine Learning Models 2.1 Definition and Types 2.2 Challenges in Model Training 2.3 Importance of Model Enhancement 3. Understanding Transfer Learning 3.1 Concept and Principles 3.2 Historical Development and Milestones 3.3 Benefits of Transfer Learning 4. Techniques in Transfer Learning 4.1 Inductive Transfer Learning 4.2 Transductive Transfer Learning 4.3 Unsupervised Transfer Learning 5. Enhancements in Artificial Intelligence Applications 5.1 Improved Accuracy and Efficiency 5.2 Application in Various Domains 5.3 Real-world Case Studies 6. Comparative Evaluation of Techniques 6.1 Criteria for Evaluation 6.2 Performance Analysis 6.3 Limitations and Challenges 7. Future Trends in Transfer Learning 7.1 Emerging Techniques 7.2 Integration with Other Technologies 7.3 Potential for Further Research 8. Conclusion and Implications 8.1 Summary of Findings 8.2 Practical Implications 8.3 Suggestions for Future Work