[Li Hongyi 2022 Machine Learning Spring] 2022-Job Description hw3

2022-Job Description HW3

from:https://www.bilibili.com/video/BV1Wv411h7kN?p=35

learning target

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Dataset introduction

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baseline

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model selection

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data augmentation

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mixup

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What needs to be changed to use mixup
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Test Time Augmentation (it needs to be done many times to show its advantages, the weight of the final test is better)

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Cross validation (need to make changes to the data set, so that you can train several models, if you don't want to do cross validation, just divide the validation set a little less, you don't need so much, just to verify whether there is overfitting)

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ensemble (average is relatively simple, and there may be a tie in voting that needs to be dealt with)

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No GPU resources to use Kaggle

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kaggle time management

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checkpoing to resume training (a trick to avoid interruptions for a long time)

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It is safer to train multiple models to do ensemble

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Experimental Tips

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Other tricks…

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Colab IO is very slow, it is recommended to use kaggle

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Q1. Data Augmentation

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Q2. Residual connection

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The provided code implements residual connections
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print dimension, don't calculate it yourself

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Training an early-stop strategy

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Origin blog.csdn.net/weixin_43154149/article/details/123942873