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Duration 21 hours
Course Outline
Foundations of Deep Learning Explainability
- Defining black-box models and their characteristics.
- The critical role of transparency in AI ecosystems.
- A review of explainability hurdles specific to neural networks.
Sophisticated XAI Techniques for Deep Learning
- Model-agnostic approaches for deep learning: LIME and SHAP.
- Layer-wise relevance propagation (LRP).
- Saliency maps and gradient-based analysis methods.
Interpreting Neural Network Decisions
- Visualization strategies for hidden layers in neural networks.
- Decoding attention mechanisms within deep learning architectures.
- Producing human-readable insights from neural network outputs.
Toolkits for Deep Learning Explainability
- An introduction to leading open-source XAI libraries.
- Leveraging Captum and InterpretML in deep learning contexts.
- Integrating explainability workflows within TensorFlow and PyTorch.
Interpretability versus Performance
- Navigating the trade-offs between predictive accuracy and model interpretability.
- Architecting deep learning models that are both interpretable and high-performing.
- Addressing bias and fairness in deep learning systems.
Practical Applications of Deep Learning Explainability
- Applying explainability principles to healthcare AI models.
- Meeting regulatory standards for AI transparency.
- Deploying interpretable deep learning models in production environments.
Ethical Dimensions of Explainable Deep Learning
- Evaluating the ethical consequences of AI transparency.
- Striking a balance between ethical AI practices and technological innovation.
- Managing privacy considerations in deep learning explainability.
Conclusions and Future Directions
Requirements
- A sophisticated grasp of deep learning principles.
- Proficiency with Python and major deep learning frameworks.
- Practical experience in developing and managing 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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