Optimisation Algorithms
Optimisation Algorithms groups the core ideas a learner needs at the 8 · hyperparameter optimisation & model selection stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Grid searchGrid search is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02Random searchRandom search is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Bayesian optimisationBayesian optimisation is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04TPETPE is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Successive halvingSuccessive halving is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06HyperbandHyperband is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Evolutionary search intuitionEvolutionary search intuition is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.