abstract

如題，簡介course 2

import

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mynotes

1. introduction to launching into ML

   • practical ML:

     • the code and the foundation knowledge
     • types of ML model
     • hystory
     • optimize ML model using loss function
     • common preoblem and how to mitigate

   • generalization:

     • how to create ML dataset
     • three identical distributed datasets and to create them in repeatbale way

   • optimization model:

     • gradient decent
     • performance metrics
     • Tensorflow playground

give yourself time to absorb the lessons

3. introduction to Practical ML
4. Supervised learning
5. ML history
6. Module quiz
7. introduction to Optimization
8. defining ML module
9. introducing loss function
10. gradient descent
11. tensor flow playground
12. performance metrics
13. Module quiz
14. introduction to Generalization and sampling
15. gneralization
16. sampling
17. Demo of splitting datasets in bigquery
18. lab: create creating repeatable datasets splits
19. lab: exploring and creating ML dataset
20. Module quiz
21. summary

murmur

距離午夜僅剩少少的18分鐘，我才剛開始打文，手飛速在鍵盤上揮舞著，但腦袋卻跟不上，手只好不時停下來等他。
對不起各位，今天光顧著看課程影片，導致文章非常之簡陋
他在introduction就說:

give yourself time to absorb the lessons
我已經有點怕怕了ㄚㄚ 畢竟那是我最缺的ㄚㄚ
另外，三分之一了耶!!!

EOL