Optimization Techniques: Convexity in Machine Learning
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Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Explores adversarial machine learning, covering the generation of adversarial examples, robustness challenges, and techniques like Fast Gradient Sign Method.
