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Related lectures (29)

Stochastic Optimization and Adaptive Gradient Methods

Explores stochastic optimization, adaptive gradient methods, recommender systems, and matrix factorization in user-item rating matrices.

Optimization: Gradient Descent and Subgradients

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

WS1S Solver: Project Structure

Explores the project structure for solving WS1S formulas and planned additions.

Optimisation in Energy Systems

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Optimization Methods

Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Optimization Problems: Standard Form

Explores optimization problems in standard form, convex optimization, and optimality criteria.

Algorithmic Complexity: Visualization and Analysis

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Thermodynamic Properties: Equations and Models

Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.

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