Course Outline
Introduction to Deep Learning Explainability
- What are black-box models?
- The importance of transparency in AI systems
- Overview of explainability challenges in neural networks
Advanced XAI Techniques for Deep Learning
- Model-agnostic methods for deep learning: LIME, SHAP
- Layer-wise relevance propagation (LRP)
- Saliency maps and gradient-based methods
Explaining Neural Network Decisions
- Visualizing hidden layers in neural networks
- Understanding attention mechanisms in deep learning models
- Generating human-readable explanations from neural networks
Tools for Explaining Deep Learning Models
- Introduction to open-source XAI libraries
- Using Captum and InterpretML for deep learning
- Integrating explainability techniques in TensorFlow and PyTorch
Interpretability vs. Performance
- Trade-offs between accuracy and interpretability
- Designing interpretable yet performant deep learning models
- Handling bias and fairness in deep learning
Real-World Applications of Deep Learning Explainability
- Explainability in healthcare AI models
- Regulatory requirements for transparency in AI
- Deploying interpretable deep learning models in production
Ethical Considerations in Explainable Deep Learning
- Ethical implications of AI transparency
- Balancing ethical AI practices with innovation
- Privacy concerns in deep learning explainability
Summary and Next Steps
Requirements
- Advanced understanding of deep learning
- Familiarity with Python and deep learning frameworks
- Experience working with neural networks
Audience
- Deep learning engineers
- AI specialists
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
