Convergence Criteria

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Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

Optimizing Recursive Queries

Explores optimizing recursive queries in database systems using Datalog and semirings, discussing the challenges and solutions in data analytics.

Optimization without Constraints: Gradient Method

Covers optimization without constraints using the gradient method to find the function's minimum.

Optimization with Constraints: KKT Conditions

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Machine Learning in Chemistry: Bayesian Reaction Optimization

Explores machine learning in chemistry, focusing on Bayesian reaction optimization and shifting the experimental burden from humans to machines.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.