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Duration 21 hours
Course Outline
Introduction to Deep Learning Explainability
- Understanding black-box models
- The significance of transparency in AI systems
- Key explainability challenges within neural networks
Advanced XAI Techniques for Deep Learning
- Model-agnostic approaches: LIME and SHAP
- Layer-wise relevance propagation (LRP)
- Saliency maps and gradient-based methods
Explaining Neural Network Decisions
- Visualizing hidden layers within neural networks
- Deciphering attention mechanisms in deep learning models
- Generating human-readable explanations from neural networks
Tools for Explaining Deep Learning Models
- Overview of open-source XAI libraries
- Leveraging Captum and InterpretML for deep learning
- Integrating explainability techniques into TensorFlow and PyTorch
Interpretability vs. Performance
- Balancing accuracy with interpretability
- Architecting deep learning models that are both interpretable and high-performing
- Addressing bias and fairness issues in deep learning
Real-World Applications of Deep Learning Explainability
- Implementing explainability in healthcare AI models
- Navigating regulatory requirements for AI transparency
- Deploying interpretable deep learning models in production environments
Ethical Considerations in Explainable Deep Learning
- Examining the ethical implications of AI transparency
- Harmonizing ethical AI practices with innovation
- Managing privacy concerns related to deep learning explainability
Summary and Next Steps
Requirements
- Solid understanding of deep learning concepts
- Proficiency in Python and deep learning frameworks
- Practical experience working with neural networks
Audience
- Deep learning engineers
- AI specialists
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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