Deep Learning: Data Representations and Neural Networks
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Delves into Topological Data Analysis, emphasizing the mathematical foundations of neural networks and exploring the manifold hypothesis and persistent homology.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
