Deep Neural Network Hyper-Parameter Optimization

Mark Whitney's article explores two approaches to optimize hyper-parameters in deep neural networks (DNNs) using Rescale's platform. The first method involves a randomized search through the Design-of-Experiments (DOE) framework and demonstrates the process using a convolutional network to classify MNIST digits, varying parameters like the number of filters and convolutional kernel size. The second approach leverages the Sequential Model-based Algorithm Configuration (SMAC) optimizer to systematically explore hyper-parameter configurations, considering additional parameters such as dropout fractions and pooling layer size. Both methods aim to enhance model performance, with the randomized search yielding a slight accuracy improvement and the SMAC optimizer offering a structured way to probe parameter space. The article provides detailed steps for setting up and executing these optimization jobs on Rescale, illustrating the potential for improved neural network performance through strategic hyper-parameter tuning.

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