Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
