A REVIEW ON HYPER-PARAMETER OPTIMISATION BY DEEP LEARNING EXPERIMENTS

Source abstract

It has been found that during the runtime of a deep learning experiment, the intermediate resultant values get removed while the processes carry forward. This removal of data forces the interim experiment to roll back to a certain initial point after which the hyper-parameters or results become difficult to obtain (mostly for a vast set of experimental data). Hyper-parameters are the various constraints/measures that a learning model requires to generalise distinct data patterns and control the learning process. A proper choice and optimization of these hyper-parameters must be made so that the learning model is capable of resolving the given machine learning problem and during training, a specific performance objective for an algorithm on a dataset is optimised. This review paper aims at presenting a Parameter Optimisation for Learning (POL) model highlighting the all-round features of a deep learning experiment via an application-based programming interface (API). This provides the means of stocking, recovering and examining parameters settings and intermediate values. To ease the process of optimisation of hyper-parameters further, the model involves the application of optimisation functions, analysis and data management. Moreover, the prescribed model boasts of a higher interactive aspect and is circulating across a number of machine learning experts, aiding further utility in data management.