Computational optimisation techniques

  • Gradient Descent, Stochastic Gradient Descent, Mini-batch Gradient Descent, Adagrad, RMS Prop, AdaDelta, and Adam are all popular deep-learning optimizers.
  • Optimisation algorithms use different techniques to test and evaluate combinations of hyperparameters, to find the optimal configurations in terms of model performance.
    The algorithms are often used within the model itself to improve its effectiveness in light of its target function too.
  • This means finding the action that optimizes (that is, maximizes or minimizes) the value of an objective function.
    For example, in a price-output decision-making problem, we may be interested in determining the output level that maximizes profits.
Computational optimization is ubiquitous in many applications in engineering and industry. In this chapter, we briefly introduce computational optimization, 
In this chapter, we briefly introduce computational optimization, the optimization algorithms commonly used in practice, and the choice of an algorithm for a 

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