1. Pre-tuning method

在现实世界里应用ML，得到大量精确标注的数据是昂贵的。

如果只有少量精确标注的数据，pre-tuning method可以帮助提升最后训练模型的精度[1]。

First pre-tuning on cheap large datasets on related domain.

Then fine-tuning on expensive well-labeled data.

As we fine-tune on precisely labeled data,

it is possible to pre-train on so-called weakly labeled data.

(i.e. 90% of the labels might be correct and 10% wrong)

2.Caveats of real-world label distributions[1]

在现实世界里，得到的数据有以下特征（相对于学术界里的平衡数据）：

- Unbalanced label distribution

- Unbalanced cost of misclassification

Solution[1],让训练集里的数据数量上更平衡:
- More data
- Change labeling:ie. merge some rare labels
- Sampling: ignore / over-or undersample / Negative minging / weighting the loss

3. Understanding black box models
在现实世界里应用ML，你不仅需要提高准确率，还需要考虑以下问题：
- understand why and how a model can make wrong predictions,
- give some intuition why our model can perform better than any previous solution,

- make sure that the model cannot be tricked.

参考：

[1] 《Applying deep learning to real-world problems》

https://medium.com/merantix/applying-deep-learning-to-real-world-problems-ba2d86ac5837

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