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Duration 21 hours
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 (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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Working from first principles in a focused way, and moving to applying case studies within the same day
