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Duration 21 hours
Course Outline
Introduction to Explainability in Deep Learning
- Defining opaque neural network architectures
- The role of transparency in accountable AI systems for government
- A summary of interpretability obstacles in neural networks
Advanced Explainable AI (XAI) Methods for Deep Learning
- Model-agnostic analytical approaches: LIME and SHAP
- Application of layer-wise relevance propagation (LRP)
- Utilization of saliency maps and gradient-based analysis
Analyzing Neural Network Decision-Making
- Visualizing internal representations in neural networks
- Deciphering attention mechanisms within deep learning models
- Deriving human-comprehensible insights from neural network outputs
Methodologies for Interpreting Deep Learning Models
- Foundations of open-source explainability toolkits
- Deployment of Captum and InterpretML in deep learning contexts
- Integration of interpretability standards in TensorFlow and PyTorch ecosystems
Interpretability Considerations vs. Model Efficacy
- Assessing the balance between predictive accuracy and transparency
- Architecting deep learning models that prioritize both interpretability and performance
- Mitigating bias and ensuring fairness in deep learning applications
Practical Implementations of Deep Learning Explainability
- Applying explainability standards in healthcare AI systems
- Compliance with regulatory mandates for AI transparency for government
- Managing interpretable deep learning models in operational environments
Ethical Frameworks for Explainable Deep Learning
- Evaluating the ethical impact of AI transparency
- Reconciling ethical AI governance with technological innovation
- Addressing data privacy concerns within explainability frameworks
Conclusions and Future Directions
Requirements
- Advanced proficiency in deep learning principles
- Proficiency in Python and associated deep learning frameworks
- Direct experience with the development and management of neural networks
Intended Participants
- Senior Deep Learning Engineers
- Artificial Intelligence 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
