Course Outline
Foundations of Deep Learning Explainability
- The nature of black-box models
- The role of transparency in AI systems
- Key challenges in explaining neural networks
Advanced XAI Methods for Deep Learning
- Model-agnostic approaches like LIME and SHAP
- Layer-wise relevance propagation (LRP)
- Saliency maps and gradient-based techniques
Interpreting Neural Network Decisions
- Visualization of hidden layers
- Analysis of attention mechanisms in deep learning
- Creating human-readable insights from neural networks
Tools for Deep Learning Explainability
- Introduction to open-source XAI libraries
- Utilizing Captum and InterpretML
- Integrating explainability into TensorFlow and PyTorch
Interpretability Versus Performance
- Balancing accuracy with interpretability
- Developing models that are both interpretable and high-performing
- Managing bias and fairness in deep learning
Practical Applications of Explainable Deep Learning
- Explainability in healthcare AI
- Compliance with AI transparency regulations
- Production deployment of interpretable models
Ethical Dimensions of Explainable Deep Learning
- The ethical impact of AI transparency
- Aligning ethical practices with innovation
- Addressing privacy issues in explainability
Conclusion and Future Directions
Requirements
- Proficient knowledge of deep learning concepts
- Proficiency in Python and relevant deep learning frameworks
- Practical experience with neural network architectures
Target 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
