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Duration 21 hours
Course Outline
Fundamentals of Deep Learning Explainability
- Defining black-box models
- The critical role of transparency in AI systems
- An overview of explainability hurdles in neural networks
Advanced XAI Methodologies for Deep Learning
- Model-agnostic approaches for deep learning: LIME and SHAP
- Layer-wise relevance propagation (LRP)
- Saliency maps and gradient-based analysis
Interpreting Neural Network Decisions
- Visualization techniques for hidden layers in neural networks
- Analyzing attention mechanisms within deep learning models
- Creating human-readable explanations from neural network outputs
Tools for Deep Learning Model Interpretation
- Overview of open-source XAI libraries
- Leveraging Captum and InterpretML for deep learning tasks
- Integrating explainability features into TensorFlow and PyTorch
Interpretability Versus Performance
- Balancing accuracy against interpretability
- Architecting deep learning models that are both interpretable and high-performing
- Addressing bias and fairness in deep learning frameworks
Practical Applications of Deep Learning Explainability
- Applying explainability in healthcare AI models
- Meeting regulatory standards for AI transparency
- Deploying interpretable deep learning models in production environments
Ethical Dimensions of Explainable Deep Learning
- Ethical implications of AI transparency
- Harmonizing ethical AI practices with technological innovation
- Privacy considerations in deep learning explainability
Conclusion and Future Directions
Requirements
- In-depth knowledge of deep learning principles
- Proficiency in Python and associated deep learning frameworks
- Practical experience in developing neural networks
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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