Course Outline

Introduction to Deep Learning Explainability

  • What are black-box models?
  • The importance 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 understanding of deep learning
  • Familiarity with Python and deep learning frameworks
  • Experience working with neural networks

Audience

  • Deep learning engineers
  • AI specialists
 21 Hours

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