Responsible & Explainable Deep Learning — Deep Learning & Neural Netw…
Understanding, trusting and safely deploying deep learning systems
Steps in Responsible & Explainable Deep Learning
- Bias in Deep Learning Models — advanced · How training data biases propagate into and amplify within model predictions
- Explainability for Neural Networks (Grad-CAM, Saliency Maps) — advanced · Visualizing which parts of an input most influenced a model's prediction
- Adversarial Examples & Robustness — advanced · Small, crafted input perturbations that fool models with high confidence
- Ethical Considerations in Deep Learning — advanced · Dual-use concerns, deepfakes, privacy and the responsibility that comes with powerful generative models
Part of
- Deep Learning & Neural Networks roadmap — the full learning path