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    Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration

    Data science is facing an overwhelming demand for CPU cycles as scientists try to work with datasets that are growing in complexity faster than Moore’s Law can keep up....

    HyperOpt: Bayesian Hyperparameter Optimization

    This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the...
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    Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines

    This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E....
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