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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
21 Hours
Testimonials (5)
Great contact with participants, practical knowledge which is highly valued. Adjustment of pace / speed. A huge plus, a mega-positive instructor, it's a shame that the training lasted only 2 days.
Marcin Mikielewicz - TECNOBIT SLU
Course - Introduction Deep Learning & Réseaux de neurones pour l’ingénieur
Machine Translated
The instructors have extensive theoretical and practical knowledge. The instructors are communicative. During the course, participants could ask questions and receive satisfying answers.
Kamil Kurek - ING Bank Slaski S.A.; Kamil Kurek Programowanie
Course - Understanding Deep Neural Networks
Machine Translated
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Trainer explained complex and advanced topics very clearly.
Leszek K
Course - Artificial Intelligence Overview
Machine Translated
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
