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Related lectures (23)
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Stochastic Gradient Descent: Optimization Techniques
Explores Stochastic Gradient Descent with Averaging, comparing it with Gradient Descent, and discusses challenges in non-convex optimization and sparse recovery techniques.
Splines and Imaging: From Compressed Sensing to Deep Neural Nets
Explores the optimality of splines for imaging and deep neural networks, demonstrating sparsity and global optimality with spline activations.
Relevance Vector Machine: Addressing SVM Shortcomings
Introduces the Relevance Vector Machine as a sparse solution to SVM shortcomings.
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