The More Data, the Better the AI, Isn't It?

Overview

Exploring data quality challenges in AI and deep learning, with insights on maintaining high-quality datasets for optimal algorithm performance in document data extraction.

Syllabus

• Introduction to Data Quality in AI
Importance of Data Quality
Overview of AI and Deep Learning
• Understanding Data in AI Models
Types of Data: Structured vs. Unstructured
Introduction to Document Data Extraction
• Data Quality Dimensions
Accuracy and Completeness
Consistency and Timeliness
Relevance and Validity
• Data Collection and Preparation
Sources of Data for AI
Strategies for Data Cleaning
Handling Missing and Noisy Data
• Challenges in Document Data Extraction
Optical Character Recognition (OCR) Issues
Data Annotation and Labeling Challenges
Handling Complex and Unstructured Documents
• Tools and Techniques for Ensuring Data Quality
Data Quality Assessment Frameworks
Automation in Data Cleaning
Use of AI to Improve Data Quality
• Maintaining High-Quality Datasets
Continuous Monitoring and Validation
Importance of Feedback Loops
Data Governance and Best Practices
• Impact of Data Quality on AI Performance
Real-World Case Studies
How Data Quality Affects Model Accuracy and Bias
• Future Trends in Data Quality for AI
Emerging Technologies in Data Quality Management
The Role of Synthetic Data
• Wrap-Up and Course Conclusion
Key Takeaways
Discussion on Industry Applications and Ethics
Final Q&A Session
• Additional Resources and Further Reading

Subjects

Conference Talks