Hyperparameter optimization for Neural Networks — NeuPy

Hyperparameter optimization for Neural Networks Contents Hyperparameter optimization for Neural Networks Introduction Hyperparameter optimization Grid Search Random Search Hand-tuning Bayesian Optimization Gaussian Process Acquisition Function Find number of hidden units Disadvantages of GP with EI Tree-structured Parzen Estimators (TPE) Overview Hyperparameter optimization for MNIST dataset Disad

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“In this article, I would like to show a few different hyperparameter selection methods. Grid Search, Random Search, Hand-tuning, Gaussian Process with Expected Improvement, Tree-structured Parzen Estimators (TPE)”