> For the complete documentation index, see [llms.txt](https://sejkai.gitbook.io/academic/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sejkai.gitbook.io/academic/deep-learning/structuring-machine-learning-projects.md).

# Structuring Machine Learning Projects

在 Structuring Machine Learning Projects 中

將傳授一些在別的地方不會說的 experiences

這些都是來自吳恩達的 "industry experience"

* 知道如何 diagnose errors in maching learning system
* 能夠優先找到正確的 implement directions 並且減少錯誤
* 了解複雜的 ML settings
  * mismatched training / test sets
  * comparing to and/or surpassing humal-level performance
* 學會 End-to-end learning, transfer learning, multi-task learning

他見過太多 teams 浪費數月、數年在進行專案，只因為沒搞懂以上這些事情

所以這堂課可以 save 你非常多時間

這將是 Deep learning 的第三堂課 !

## ML Strategy (1)

* Orthogonalization
* Single number evaluation metric
* Train/dev/test distributions
* human-level performance
* Avoidable bias
* model performance

## ML Strategy (2)

* error analysis
* clean up incorrect labeled data
* Build your first system quickly, then iterate
* Training and testing on different distributions
* Bias and Variance with mismatched data distributions
* Addressing data mismatch
* Transfer learning
* Multi-task learning
* End-to-end deep learning