Gradient Descent-Based Optimization Task

A Gradient Descent-Based Optimization Task is a metric-space optimization task that involves optimizing parameters of a system or model using a Gradient Descent Algorithm.

• Context:
  • This task focuses on minimizing or maximizing an Objective Function through iterative adjustments to the parameters based on the gradients of the function.
  • It can (often) be applied to Machine Learning Models to adjust parameters such as weights during training.
  • It can range from simple tasks like Linear Regression to complex neural network training in Deep Learning.
  • It can be executed using various forms of gradient descent algorithms, from Batch Gradient Descent Algorithm to Online Gradient Descent Algorithm, and from Exact Gradient Descent Algorithm to Approximate Gradient Descent Algorithm like Stochastic Gradient Descent (SGD).
  • It can be part of systems designed as Gradient Descent-based Optimization Systems, capable of executing tasks that utilize the gradient descent method.
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• Example(s):
  • an Artificial Neural Network Training Task that uses gradient descent to find optimal weights that minimize the loss function;
  • a Logistic Regression Task that employs gradient descent to optimize the coefficients for best model fit;
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• Counter-Example(s):
  • Evolutionary Optimization Tasks, which do not rely on gradient information but instead use methods like genetic algorithms for optimization;
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• See: Gradient Descent Optimization Algorithm, Objective Function, Batch Gradient Descent Algorithm, Stochastic Gradient Descent.

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