Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. machine-learning-yearning-book (2017). [2].

Machine Learning 52

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Heartbeat

JUNE 14, 2023

International conference on machine learning. PMLR, 2017. [2] Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. arXiv preprint arXiv:1710.09412 (2017). [7] On mixup training: Improved calibration and predictive uncertainty for deep neural networks.” CVPR workshops.

Deep Learning 59

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We perform a five-fold cross-validation to select the best model during training, and perform hyperparameter optimization to select the best settings on multiple model architecture and training parameters.

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