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Duration 21 hours
Course Outline
Foundations of Deep Learning Explainability
- Defining black-box models in machine learning
- The critical role of transparency in AI ecosystems
- Navigating the core challenges of explainability in neural networks
Advanced XAI Methodologies for Deep Learning
- Model-agnostic approaches for deep learning, such as LIME and SHAP
- Layer-wise relevance propagation (LRP)
- Saliency maps and gradient-based analysis techniques
Decoding Neural Network Decision-Making
- Visualization techniques for hidden layers in neural networks
- Analyzing attention mechanisms within deep learning models
- Creating human-readable narratives from neural network outputs
Toolsets for Deep Learning Explainability
- Overview of open-source XAI libraries
- Leveraging Captum and InterpretML for deep learning tasks
- Integrating explainability features into TensorFlow and PyTorch workflows
Interpretability and Performance Balance
- Managing the trade-offs between model accuracy and interpretability
- Architecting deep learning models that are both interpretable and high-performing
- Addressing bias and ensuring fairness in deep learning applications
Practical Applications of Deep Learning Explainability
- Applying explainability principles to healthcare AI models
- Meeting regulatory standards for AI transparency
- Implementing interpretable deep learning models in production environments
Ethical Perspectives on Explainable Deep Learning
- The ethical impact of AI transparency on society
- Harmonizing ethical AI practices with technological innovation
- Addressing privacy concerns related to deep learning explainability
Conclusions and Future Directions
Requirements
- Advanced knowledge of deep learning concepts
- Proficiency with Python and major deep learning frameworks
- Practical experience in developing and working with 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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