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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.
- 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 knowledge 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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