转载自：https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-now-supports-early-stopping-of-training-jobs/

Huibin Shen
发表于

In June 2018, we launched Amazon SageMaker Automatic Model Tuning, a feature that automatically finds well-performing hyperparameters to train a machine learning model with. Unlike model parameters learned during training, hyperparameters are set before the learning process begins. A typical example of the use of hyperparameters is the learning rate of stochastic gradient procedures. Using default hyperparameters doesn’t always yield the best model performance, and finding well-performing hyperparameters can be a non-trivial and time-consuming task. Using Automatic Model Tuning, Amazon SageMaker will automatically find well-performing hyperparameters and train your model to maximize your objective metric.

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转载自：http://feedproxy.google.com/~r/RBloggers/~3/wl0VNsrYUIM/

Jakob Gepp
发表于

We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 13th day of Christmas my true love gave to me…

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