Papers › Compare different SG-Schemes based on large least square problems
Compare different SG-Schemes based on large least square problems
Ramkrishna Acharya
This study reviews some of the popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning play a crucial role in finding better parameters of a model. Hence this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes that produced results in this work are available on https://github.com/q-viper/gradients-based-methods-on-large-least-square.
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